# Timefold Documentation

- [FAQ](https://docs.timefold.ai/timefold-solver/1.x/frequently-asked-questions.html): Answers common questions about Timefold Solver licensing, availability of pre-built models, and support for GraalVM native executables, including integration and performance considerations.
- [Introduction](https://docs.timefold.ai/timefold-solver/1.x/introduction.html): The Introduction page explains what Timefold Solver is, outlines its core planning optimization capabilities, and highlights typical use cases such as vehicle routing and employee scheduling.
- [PlanningAI Concepts](https://docs.timefold.ai/timefold-solver/1.x/planning-ai-concepts.html): Explains the core concepts of Planning AI, including goals, resources, constraints, operations research, and how Planning AI solves complex planning and scheduling problems.
- [Adjusting constraints at runtime](https://docs.timefold.ai/timefold-solver/1.x/constraints-and-score/constraint-configuration.html): This page explains how to define, override, and adjust constraint weights at runtime in Timefold Solver, including serialization and parameterization techniques for dynamic optimization scenarios.
- [Load balancing and fairness](https://docs.timefold.ai/timefold-solver/1.x/constraints-and-score/load-balancing-and-fairness.html): Explains how to implement load balancing and fairness constraints in Timefold Solver, including definitions of fairness, constraint modeling, and recommendations for score calculation types.
- [Constraints and Score: Overview](https://docs.timefold.ai/timefold-solver/1.x/constraints-and-score/overview.html): This page explains how Timefold Solver uses constraints to calculate and interpret solution scores, covering score terminology, constraint types, weights, levels, and best practices for modeling business requirements.
- [Performance tips and tricks](https://docs.timefold.ai/timefold-solver/1.x/constraints-and-score/performance.html): This page provides practical strategies and considerations for improving solver performance by optimizing move evaluation speed, score calculation, constraint usage, and avoiding common pitfalls.
- [Score calculation](https://docs.timefold.ai/timefold-solver/1.x/constraints-and-score/score-calculation.html): This page explains how score calculation works in Timefold, covering easy score calculators, the Constraint Streams API, incremental score calculation, and implementing custom score calculators.
- [Understanding the score](https://docs.timefold.ai/timefold-solver/1.x/constraints-and-score/understanding-the-score.html): Explains how to interpret and break down the optimization score, identify broken constraints, analyze justifications and indicted objects, and visualize constraint impacts using the SolutionManager and related APIs.
- [Design patterns](https://docs.timefold.ai/timefold-solver/1.x/design-patterns/design-patterns.html): This page explains Timefold Solver design patterns, including domain modeling guidelines, time assignment strategies, and cloud architecture patterns for effective constraint-solving project design.
- [Enterprise Edition](https://docs.timefold.ai/timefold-solver/1.x/enterprise-edition/enterprise-edition.html): Enterprise Edition documents the commercial version of Timefold Solver, detailing exclusive features like nearby selection, multi-threaded solving, partitioned search, and setup instructions for Maven and Gradle integration.
- [timefold-solver/1.x/integration/config-properties.html](https://docs.timefold.ai/timefold-solver/1.x/integration/config-properties.html): Documents all Timefold Solver configuration properties for Quarkus and Spring Boot, including parallel solver count, solver XML, random seed, environment mode, termination, and benchmarking options.
- [Integration](https://docs.timefold.ai/timefold-solver/1.x/integration/integration.html): This page explains how to integrate Timefold Solver with Java applications, covering database persistence (JPA/Hibernate), XML/JSON serialization (JAXB/Jackson), and REST service exposure using annotated POJOs.
- [Construction heuristics](https://docs.timefold.ai/timefold-solver/1.x/optimization-algorithms/construction-heuristics.html): Explains construction heuristics in optimization, detailing algorithms like First Fit, First Fit Decreasing, Weakest Fit, and their configuration and scaling options for generating initial solutions.
- [Exhaustive search](https://docs.timefold.ai/timefold-solver/1.x/optimization-algorithms/exhaustive-search.html): This page explains the Exhaustive Search optimization algorithms, including Brute Force and Branch and Bound, their configuration, scalability limitations, and why they are impractical for real-world problems.
- [Local search](https://docs.timefold.ai/timefold-solver/1.x/optimization-algorithms/local-search.html): The page explains how the Local Search optimization algorithm works, including its step-by-step process, configuration options, and concepts like MoveSelector, Acceptor, Forager, and Variable Neighborhood Descent.
- [Move Selector reference](https://docs.timefold.ai/timefold-solver/1.x/optimization-algorithms/move-selector-reference.html): Documents all available MoveSelector implementations for optimization algorithms, detailing their configuration, behavior, and use cases for selecting and applying moves in planning solutions.
- [Neighborhoods: A new way to define custom moves](https://docs.timefold.ai/timefold-solver/1.x/optimization-algorithms/neighborhoods.html): The Neighborhoods API introduces a flexible way to define custom moves for optimization in Timefold Solver, allowing users to create, combine, and experiment with advanced move types beyond traditional move selectors.
- [Optimization Algorithms: Overview](https://docs.timefold.ai/timefold-solver/1.x/optimization-algorithms/overview.html): This page provides an overview of optimization algorithms in Timefold Solver, explaining search space size, optimality, scalability, and the main algorithm families supported for solving planning problems.
- [Overview](https://docs.timefold.ai/timefold-solver/1.x/quickstart/overview.html): The Overview page introduces Timefold Solver quick start guides for building and running school timetable optimization applications using Java, Kotlin, Quarkus, or Spring Boot.
- [Responding to change](https://docs.timefold.ai/timefold-solver/1.x/responding-to-change/responding-to-change.html): This page explains how Timefold Solver supports adapting planning solutions to changes in problem facts, covering backup planning, overconstrained planning, real-time planning, and multi-stage planning strategies.
- [Backwards compatibility](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/backwards-compatibility.html): Explains which Timefold Solver API packages are guaranteed backwards compatible, which are not, and how preview features differ in stability and activation.
- [timefold-solver/1.x/upgrading-timefold-solver/framework-version-warning.html](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/framework-version-warning.html): Explains that automatic migrations do not update framework versions and advises consulting integration guides for resolving compatibility issues with Spring or Quarkus.
- [Upgrading Timefold Solver: Overview](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/overview.html): Overview of how to upgrade from OptaPlanner to Timefold Solver, upgrade to the latest Timefold Solver version, and locate detailed API upgrade recipes.
- [Upgrade from OptaPlanner](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/upgrade-from-optaplanner.html): Explains how to upgrade from OptaPlanner to Timefold Solver, covering both automatic migration steps and manual changes required for compatibility.
- [Upgrade to the latest version](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/upgrade-to-latest-version.html): This page provides step-by-step instructions and tooling for upgrading your Timefold Solver codebase to the latest version, including both automated and manual migration guidance.
- [Benchmarking and tweaking](https://docs.timefold.ai/timefold-solver/1.x/using-timefold-solver/benchmarking-and-tweaking.html): This page explains how to use Timefold Solver's benchmarking tools to compare solver configurations, set up benchmarks, and aggregate benchmark results for optimization problems.
- [Configuring Timefold Solver](https://docs.timefold.ai/timefold-solver/1.x/using-timefold-solver/configuration.html): Explains how to configure a Timefold Solver instance using XML files or the SolverConfig API, including model definition, score calculation, optimization algorithms, domain access, and custom properties.
- [Modeling planning problems](https://docs.timefold.ai/timefold-solver/1.x/using-timefold-solver/modeling-planning-problems.html): This page explains how to model planning problems in Timefold Solver, detailing planning entities, variables, problem facts, solutions, and domain modeling rules with examples.
- [Using Timefold Solver: Overview](https://docs.timefold.ai/timefold-solver/1.x/using-timefold-solver/overview.html): The page explains the main steps for solving a planning problem with Timefold Solver, including modeling, configuring the solver, loading data, and running the solve process.
- [Running Timefold Solver](https://docs.timefold.ai/timefold-solver/1.x/using-timefold-solver/running-the-solver.html): Explains how to use the Timefold Solver and SolverManager APIs to solve planning problems, configure environment modes, and handle multi-threaded and live solution scenarios.
- [Hello World Quick Start Guide](https://docs.timefold.ai/timefold-solver/1.x/quickstart/hello-world/hello-world-quickstart.html): This page provides step-by-step instructions for building a simple Java or Kotlin command-line application that uses Timefold to optimize a school timetable with AI constraint solving.
- [Quarkus Quick Start Guide](https://docs.timefold.ai/timefold-solver/1.x/quickstart/quarkus/quarkus-quickstart.html): This page explains how to create a Quarkus application that uses Timefold's constraint solving AI, including setup, dependencies, model definition, solver service, and configuration.
- [Vehicle Routing Quick Start Guide](https://docs.timefold.ai/timefold-solver/1.x/quickstart/quarkus-vehicle-routing/quarkus-vehicle-routing-quickstart.html): This page explains how to build a Quarkus REST application that uses Timefold Solver to optimize the Vehicle Routing Problem (VRP) with step-by-step instructions and example code.
- [Define the constraints and calculate the score](https://docs.timefold.ai/timefold-solver/1.x/quickstart/quarkus-vehicle-routing/vehicle-routing-constraints.html): Explains how to define hard and soft constraints for vehicle routing and implement score calculation using a ConstraintProvider with the Constraint Streams API.
- [Model the domain objects](https://docs.timefold.ai/timefold-solver/1.x/quickstart/quarkus-vehicle-routing/vehicle-routing-model.html): This page explains how to model the domain objects for vehicle routing, including the Location, Vehicle, and Visit classes, and their roles in the optimization process.
- [Gather the domain objects in a planning solution](https://docs.timefold.ai/timefold-solver/1.x/quickstart/quarkus-vehicle-routing/vehicle-routing-solution.html): Describes how the VehicleRoutePlan class aggregates all Vehicle and Visit instances for a dataset, serving as both the input and output of a vehicle routing planning solution.
- [timefold-solver/1.x/quickstart/shared/constrainttests.html](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/constrainttests.html): This page explains how to write and use unit tests for constraint logic in a Timefold School Timetabling project using the ConstraintVerifier API.
- [timefold-solver/1.x/quickstart/shared/java-logback-config.html](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/java-logback-config.html): Explains how to configure Logback logging for a Java project by creating a `logback.xml` file with a console appender and log level settings.
- [timefold-solver/1.x/quickstart/shared/java-prerequisites.html](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/java-prerequisites.html): Lists the required Java Development Kit version, build tools (Maven or Gradle), and recommended IDEs needed before starting Java-based quickstart guides.
- [timefold-solver/1.x/quickstart/shared/solutionsourcecode.html](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/solutionsourcecode.html): Instructions for obtaining the complete solution source code for the quickstart by cloning the repository or downloading an archive and running the example.
- [timefold-solver/1.x/quickstart/shared/try-the-application.html](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/try-the-application.html): Demonstrates how to test the running application by sending a POST request to the REST service and interpreting the resulting timetable solution and server logs.
- [timefold-solver/1.x/quickstart/shared/whatyoubuild.html](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/whatyoubuild.html): The page explains how to build a REST application that uses AI constraint solving to automatically generate optimized school timetables for students and teachers.
- [Spring Boot Quick Start Guide](https://docs.timefold.ai/timefold-solver/1.x/quickstart/spring-boot/spring-boot-quickstart.html): This page explains how to build a Spring Boot application that uses Timefold's constraint solving AI, covering setup, dependencies, domain modeling, solver integration, and configuration.
- [Chained planning variable to planning list variable](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/migration-guides/chained-variables-to-planning-list-variable.html): Explains how to migrate from the deprecated chained planning variable pattern to the new planning list variable approach in Timefold Solver for sequence-based optimization problems.
- [Variable Listeners to Custom Shadow Variables](https://docs.timefold.ai/timefold-solver/1.x/upgrading-timefold-solver/migration-guides/variable-listeners-to-custom-shadow-variables.html): Explains how to migrate from the deprecated custom VariableListener pattern to the new declarative custom shadow variable approach in Timefold Solver, including migration steps and code examples.
- [Define the constraints and calculate the score](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/school-timetabling/school-timetabling-constraints.html): This page explains how to define hard and soft constraints for the school timetabling problem and implement score calculation using both EasyScoreCalculator and ConstraintProvider approaches.
- [Model the domain objects](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/school-timetabling/school-timetabling-model.html): This page explains how to model the core domain objects—Timeslot, Room, and Lesson—for the school timetabling problem, including their structure and required Timefold Solver annotations.
- [Gather the domain objects in a planning solution](https://docs.timefold.ai/timefold-solver/1.x/quickstart/shared/school-timetabling/school-timetabling-solution.html): Explains how the Timetable class in a school timetabling solution aggregates Timeslot, Room, and Lesson domain objects, serving as both the problem input and the planning solution output for Timefold Solver.
- [FAQ](https://docs.timefold.ai/timefold-solver/latest/frequently-asked-questions.html): The FAQ page answers common questions about Timefold Solver licensing, pre-built PlanningAI models, and compatibility with GraalVM native applications.
- [Introduction](https://docs.timefold.ai/timefold-solver/latest/introduction.html): The Introduction page explains what Timefold Solver is, outlines its core planning optimization capabilities, and describes typical use cases and integration options for new users.
- [PlanningAI concepts](https://docs.timefold.ai/timefold-solver/latest/planning-ai-concepts.html): This page explains the core concepts of Planning AI, including goals, resources, constraints, feasibility, and how Planning AI solves complex scheduling and optimization problems.
- [Plus/Enterprise Editions](https://docs.timefold.ai/timefold-solver/latest/commercial-editions/commercial-editions.html): The Plus and Enterprise Editions page explains the commercial versions of Timefold Solver, detailing their exclusive features, licensing, and a comparison with the Community Edition.
- [Installing Timefold Solver Plus & Enterprise](https://docs.timefold.ai/timefold-solver/latest/commercial-editions/installation.html): Describes how to configure a license key and update Maven dependencies to install and use the commercial Plus or Enterprise editions of Timefold Solver.
- [Multistage moves](https://docs.timefold.ai/timefold-solver/latest/commercial-editions/multistage-moves.html): Describes multistage moves in the enterprise edition, detailing how they are composed of sequential stages, their evaluators, stage providers, and configuration options for advanced move selection.
- [Performance improvements](https://docs.timefold.ai/timefold-solver/latest/commercial-editions/performance-improvements.html): Describes the performance improvements in Timefold Solver Enterprise, including faster shadow variable updates and automatic node sharing for constraint streams, with configuration and usage details.
- [Adjusting constraints at runtime](https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/constraint-configuration.html): This page explains how to define, override, and parameterize constraint weights at runtime in Timefold Solver, including using the ConstraintWeightOverrides API and exposing parameters as problem facts.
- [Load balancing and fairness](https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/load-balancing-and-fairness.html): Explains how to implement load balancing and fairness constraints in Timefold Solver, including definitions of fairness, constraint examples, and recommendations for score calculation types.
- [Constraints and Score: Overview](https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/overview.html): Explains how Timefold Solver uses constraints and scoring to evaluate, compare, and optimize planning solutions, including score terminology, constraint types, weights, and levels.
- [Performance tips and tricks](https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/performance.html): This page provides practical strategies and considerations for improving Timefold Solver's move evaluation and score calculation performance, including incremental calculation, constraint design, and benchmarking techniques.
- [Score calculation](https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/score-calculation.html): This page explains how score calculation works in Timefold, covering easy score calculators, the Constraint Streams API, incremental calculation, and implementing custom score calculators.
- [Understanding the score (Score Analysis)](https://docs.timefold.ai/timefold-solver/latest/constraints-and-score/understanding-the-score.html): Explains how to analyze and break down the optimization score using the SolutionManager and ScoreAnalysis APIs, including identifying broken constraints, their causes, and comparing solution differences.
- [Design patterns](https://docs.timefold.ai/timefold-solver/latest/design-patterns/design-patterns.html): This page explains Timefold Solver design patterns, including domain modeling guidelines, planning entity relationships, time assignment strategies, and cloud architecture usage patterns for constraint-solving projects.
- [Integration](https://docs.timefold.ai/timefold-solver/latest/integration/integration.html): This page explains how to integrate Timefold Solver with Java applications, covering database persistence (JPA/Hibernate), XML/JSON serialization (JAXB/Jackson), and REST service exposure using annotated domain POJOs.
- [Construction heuristics](https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/construction-heuristics.html): Explains construction heuristics in optimization, detailing algorithms like First Fit, First Fit Decreasing, Weakest Fit, and their configuration and scaling options for generating initial solutions.
- [Exhaustive search](https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/exhaustive-search.html): This page explains the Exhaustive Search optimization algorithms, including Brute Force and Branch and Bound, their configuration, and why they are impractical for real-world problems due to scalability limitations.
- [Local search](https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/local-search.html): The Local Search page explains how the local search optimization algorithm iteratively improves a single solution using configurable move selection, acceptance, and foraging strategies, including Tabu Search and Variable Neighborhood Descent.
- [Move Selector reference](https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/move-selector-reference.html): This page documents the Move Selector API in Timefold Solver, detailing available move selector types, configuration options, composition patterns, and guidance for customizing move selection in optimization algorithms.
- [Neighborhoods: A new way to define custom moves](https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/neighborhoods.html): The Neighborhoods API preview feature in Timefold Solver enables users to define custom move types for optimization algorithms, offering a flexible alternative to traditional move selectors.
- [Optimization Algorithms: Overview](https://docs.timefold.ai/timefold-solver/latest/optimization-algorithms/overview.html): This page explains the fundamentals of optimization algorithms in Timefold Solver, including search space size, algorithm families, scalability, and how optimal solutions are approached.
- [Overview](https://docs.timefold.ai/timefold-solver/latest/quickstart/overview.html): The Overview page introduces the available quick start guides for building and running Timefold Solver optimization models, including Hello World, Quarkus, and Spring Boot examples.
- [Responding to change](https://docs.timefold.ai/timefold-solver/latest/responding-to-change/responding-to-change.html): This page explains how Timefold Solver supports adapting planning solutions to changes in problem facts, including backup, overconstrained, real-time, and multi-stage planning strategies.
- [Backwards compatibility](https://docs.timefold.ai/timefold-solver/latest/upgrading-timefold-solver/backwards-compatibility.html): Explains which Timefold Solver API packages are guaranteed backwards compatible, which implementation classes are not, and how preview features are handled regarding stability and activation.
- [timefold-solver/latest/upgrading-timefold-solver/framework-version-warning.html](https://docs.timefold.ai/timefold-solver/latest/upgrading-timefold-solver/framework-version-warning.html): Explains that automatic migrations do not update framework versions and advises consulting integration guides for resolving compatibility issues with Spring or Quarkus.
- [Upgrading Timefold Solver: Overview](https://docs.timefold.ai/timefold-solver/latest/upgrading-timefold-solver/overview.html): This page explains how to upgrade to the latest version of Timefold Solver and provides detailed upgrade instructions for major API changes.
- [Upgrade from Timefold Solver 1.x to 2.x](https://docs.timefold.ai/timefold-solver/latest/upgrading-timefold-solver/upgrade-from-v1.html): This page details the steps, automated tooling, and manual code changes required to upgrade a project from Timefold Solver 1.x to 2.0.0, including all breaking API changes.
- [Benchmarking and tweaking](https://docs.timefold.ai/timefold-solver/latest/using-timefold-solver/benchmarking-and-tweaking.html): Explains how to use Timefold Solver's benchmarking tools to compare, configure, and aggregate solver algorithm performance for optimal planning solution selection.
- [Configuring Timefold Solver](https://docs.timefold.ai/timefold-solver/latest/using-timefold-solver/configuration.html): Explains how to configure a Timefold Solver instance using XML files or the SolverConfig API, including model definition, score function, optimization algorithms, and custom properties.
- [Modeling planning problems](https://docs.timefold.ai/timefold-solver/latest/using-timefold-solver/modeling-planning-problems.html): This page explains how to model planning problems in Timefold Solver, detailing planning entities, variables, problem facts, solutions, and rules for domain model structure and inheritance.
- [Using Timefold Solver: Overview](https://docs.timefold.ai/timefold-solver/latest/using-timefold-solver/overview.html): The page outlines the main steps for solving a planning problem with Timefold Solver, including modeling, configuring, loading data, and running the solver.
- [Running Timefold Solver](https://docs.timefold.ai/timefold-solver/latest/using-timefold-solver/running-the-solver.html): Explains how to use the Timefold Solver and SolverManager APIs to solve planning problems, including single-threaded and multi-threaded execution, solution retrieval, and progress reporting.
- [Hello World Quick Start Guide](https://docs.timefold.ai/timefold-solver/latest/quickstart/hello-world/hello-world-quickstart.html): This page provides a step-by-step guide to building a simple Java or Kotlin command-line application that uses Timefold Solver to optimize a school timetable with AI-driven constraint solving.
- [Quarkus Quick Start Guide](https://docs.timefold.ai/timefold-solver/latest/quickstart/quarkus/quarkus-quickstart.html): This page provides step-by-step instructions for building a Quarkus application that uses Timefold's constraint solving AI, including setup, dependencies, model code, and solver integration.
- [Vehicle Routing Quick Start Guide](https://docs.timefold.ai/timefold-solver/latest/quickstart/quarkus-vehicle-routing/quarkus-vehicle-routing-quickstart.html): This page provides step-by-step instructions for building a Vehicle Routing Problem (VRP) optimization REST application using Quarkus and Timefold Solver, including setup, dependencies, and testing.
- [Define the constraints and calculate the score](https://docs.timefold.ai/timefold-solver/latest/quickstart/quarkus-vehicle-routing/vehicle-routing-constraints.html): Explains how to define hard and soft constraints for vehicle routing and implement a VehicleRoutingConstraintProvider to calculate the solution score using Timefold Solver's Constraint Streams API.
- [Model the domain objects](https://docs.timefold.ai/timefold-solver/latest/quickstart/quarkus-vehicle-routing/vehicle-routing-model.html): Explains how to implement the domain model for vehicle routing, detailing the Location, Vehicle, and Visit classes and their roles in route assignment and solver integration.
- [Gather the domain objects in a planning solution](https://docs.timefold.ai/timefold-solver/latest/quickstart/quarkus-vehicle-routing/vehicle-routing-solution.html): The page explains how the VehicleRoutePlan class aggregates all Vehicle and Visit instances in a dataset, serving as both the input and output for a vehicle routing planning solution.
- [Spring Boot Quick Start Guide](https://docs.timefold.ai/timefold-solver/latest/quickstart/spring-boot/spring-boot-quickstart.html): This page explains how to create a Spring Boot application that uses Timefold's constraint solving AI, including setup, dependencies, model, constraints, REST API, and configuration.
- [Chained planning variable to planning list variable](https://docs.timefold.ai/timefold-solver/latest/upgrading-timefold-solver/migration-guides/chained-variables-to-planning-list-variable.html): This page details how to migrate from the deprecated chained planning variable approach to the planning list variable model in Timefold Solver, including code changes and domain model updates.
- [Variable Listeners to Custom Shadow Variables](https://docs.timefold.ai/timefold-solver/latest/upgrading-timefold-solver/migration-guides/variable-listeners-to-custom-shadow-variables.html): This page explains how to migrate from the deprecated VariableListener pattern to the new declarative custom shadow variable approach in Timefold Solver 2.0, including migration steps and code examples.
- [Benchmarking and tweaking](https://docs.timefold.ai/timefold-solver/0.8.x/benchmarking-and-tweaking/benchmarking-and-tweaking.html): This page explains how to use Timefold's benchmarking tools to compare solver configurations, set up and run benchmarks, and aggregate benchmark reports for optimization problems.
- [Timefold configuration](https://docs.timefold.ai/timefold-solver/0.8.x/configuration/configuration.html): Describes how to configure and use Timefold's Solver for planning problems, including XML and Java API configuration, model setup, and runtime solution management.
- [Constraint streams score calculation](https://docs.timefold.ai/timefold-solver/0.8.x/constraint-streams/constraint-streams.html): The page explains how to use the Constraint Streams API in Java for efficient, incremental score calculation in optimization problems, including API usage, configuration, and testing practices.
- [Construction heuristics](https://docs.timefold.ai/timefold-solver/0.8.x/construction-heuristics/construction-heuristics.html): Explains construction heuristics for optimization, detailing First Fit, First Fit Decreasing, Weakest Fit, and Weakest Fit Decreasing algorithms, their configurations, and scaling techniques.
- [Design patterns](https://docs.timefold.ai/timefold-solver/0.8.x/design-patterns/design-patterns.html): This page documents reusable design patterns, domain modeling guidelines, and architectural strategies for building constraint-solving applications with Timefold, including entity relationships, planning variables, and cloud deployment approaches.
- [Development](https://docs.timefold.ai/timefold-solver/0.8.x/development/development.html): Explains the development methodology, code structure, and best practices for contributing to Timefold, including guidelines for error handling, generics, and subsystem lifecycle management.
- [Evolutionary algorithms](https://docs.timefold.ai/timefold-solver/0.8.x/evolutionary-algorithms/evolutionary-algorithms.html): Describes the concept of evolutionary algorithms, notes their current lack of implementation in Timefold, and discusses the status and performance of genetic algorithms relative to local search.
- [Exhaustive search](https://docs.timefold.ai/timefold-solver/0.8.x/exhaustive-search/exhaustive-search.html): Explains the Exhaustive Search solver in Timefold, detailing Brute Force and Branch and Bound algorithms, configuration options, and their severe scalability limitations for real-world problems.
- [Hyperheuristics](https://docs.timefold.ai/timefold-solver/0.8.x/hyperheuristics/hyperheuristic.html): The Hyperheuristics page explains how to automate the selection of solver heuristics for different data sets by adjusting Solver configurations based on dataset characteristics.
- [timefold-solver/0.8.x/integration/config-properties.html](https://docs.timefold.ai/timefold-solver/0.8.x/integration/config-properties.html): Documents all available Timefold configuration properties for Quarkus and Spring Boot, including solver, benchmarking, parallelism, domain access, constraint streams, and termination settings.
- [Integration](https://docs.timefold.ai/timefold-solver/0.8.x/integration/integration.html): Describes how to integrate Timefold with Java technologies such as JPA, Hibernate, JAXB, and Jackson for persisting and serializing planning solutions and scores.
- [Timefold introduction](https://docs.timefold.ai/timefold-solver/0.8.x/introduction/introduction.html): The Timefold introduction page explains what Timefold is, the types of planning and optimization problems it solves, and the core concepts of constraint satisfaction programming.
- [Local search](https://docs.timefold.ai/timefold-solver/0.8.x/local-search/local-search.html): The Local Search page explains how the Local Search optimization algorithm works, including its step-by-step process, configuration options, and the Variable Neighborhood Descent variant.
- [Move and neighborhood selection](https://docs.timefold.ai/timefold-solver/0.8.x/move-and-neighborhood-selection/move-and-neighborhood-selection.html): This page explains the concept of a Move in optimization, how MoveSelectors generate and configure moves, and details the various built-in and customizable move selection strategies.
- [Optimization algorithms](https://docs.timefold.ai/timefold-solver/0.8.x/optimization-algorithms/optimization-algorithms.html): This page explains the types of optimization algorithms supported by Timefold, their scalability, how they handle large search spaces, and practical considerations for finding optimal or near-optimal solutions.
- [Partitioned search](https://docs.timefold.ai/timefold-solver/0.8.x/partitioned-search/partitioned-search.html): Partitioned search describes how to split large optimization problems into independent partitions for parallel solving, including configuration, custom partitioners, and CPU usage management in Timefold.
- [Release Notes](https://docs.timefold.ai/timefold-solver/0.8.x/release-notes/release-notes.html): Documents where to find the latest upgrade instructions for Timefold Solver and directs users to the official documentation for current release notes.
- [Repeated planning](https://docs.timefold.ai/timefold-solver/0.8.x/repeated-planning/repeated-planning.html): Repeated planning covers strategies for adapting optimization solutions to changing problem facts, including backup planning, overconstrained planning, continuous planning, and multi-stage planning techniques.
- [Score calculation](https://docs.timefold.ai/timefold-solver/0.8.x/score-calculation/score-calculation.html): This page explains how Timefold calculates solution scores using constraints, weights, and levels, and describes techniques for formalizing, analyzing, and testing business constraints.
- [Shadow variable](https://docs.timefold.ai/timefold-solver/0.8.x/shadow-variable/shadow-variable.html): A shadow variable in Timefold is a planning variable whose value is automatically derived from genuine planning variables, including built-in types like bi-directional, anchor, and custom shadow variables, and is updated via VariableListeners during optimization.
- [Use cases and examples](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/use-cases-and-examples.html): Provides a collection of optimization use cases and examples, including N-Queens, cloud balancing, scheduling, timetabling, routing, and resource allocation scenarios.
- [Hello world Java quick start](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/hello-world/hello-world-quickstart.html): This page provides step-by-step instructions for building a simple Java command-line application that uses Timefold to optimize a school timetable with constraint solving.
- [Overview](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/overview/overview-quickstarts.html): The overview introduces three Timefold quick start guides—Hello World Java, Quarkus Java, and Spring Boot Java—for building applications that optimize school timetables.
- [Quarkus Java quick start](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/quarkus/quarkus-quickstart.html): This page provides step-by-step instructions for building a Quarkus Java REST application that uses Timefold AI to optimize school timetables with constraint solving.
- [Define the constraints and calculate the score](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/school-timetabling/school-timetabling-constraints.html): This page explains how to define hard and soft constraints for school timetabling and calculate the solution score using both EasyScoreCalculator and ConstraintProvider approaches.
- [Model the domain objects](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/school-timetabling/school-timetabling-model.html): This page explains how to model the school timetabling domain by implementing the Timeslot, Room, and Lesson classes, including their roles as problem facts and planning entities.
- [Gather the domain objects in a planning solution](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/school-timetabling/school-timetabling-solution.html): This page explains how the TimeTable class in a school timetabling solution aggregates all Timeslot, Room, and Lesson domain objects, serving as both the problem input and the planning solution output for Timefold.
- [Spring Boot Java quick start](https://docs.timefold.ai/timefold-solver/0.8.x/quickstart/spring-boot/spring-boot-quickstart.html): This page explains how to create a Spring Boot Java application that uses Timefold's AI constraint solver to optimize a school timetable, including setup, dependencies, and REST API implementation.
- [Hospital bed planning (PAS - Patient Admission Scheduling)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/bed-allocation/bed-allocation.html): Explains the hospital bed planning (Patient Admission Scheduling) problem, detailing constraints, domain model, and real-world dataset sizes for assigning patients to beds each night.
- [Cloud balancing](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/cloud-balancing/cloud-balancing.html): Cloud balancing documents how to assign processes to cloud computers while satisfying hardware constraints and minimizing maintenance costs, using domain modeling and optimization with Timefold.
- [Conference scheduling](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/conference-scheduling/conference-scheduling.html): Explains how to assign conference talks to timeslots and rooms using configurable hard, medium, and soft constraints, with input and output via editable Excel files.
- [Course timetabling (ITC 2007 Track 3 - Curriculum Course Scheduling)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/course-timetabling/course-timetabling.html): Describes the curriculum course timetabling problem from ITC 2007 Track 3, detailing its hard and soft constraints, problem sizes, and domain model for scheduling lectures.
- [Exam timetabling (ITC 2007 track 1 - Examination)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/exam-timetabling/exam-timetabling.html): Documents the exam timetabling problem from ITC 2007 track 1, detailing its constraints, dataset sizes, and the examination domain model used for scheduling exams into periods and rooms.
- [Examples overview](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/examples-overview/examples-overview.html): This page provides an overview of the example problems included with Timefold, summarizing their domains, sizes, contest relevance, and special features used in each example.
- [Flight crew scheduling](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/flight-crew-scheduling/flight-crew-scheduling.html): Explains how to assign flights to pilots and flight attendants while satisfying skill requirements, scheduling constraints, and balancing workloads in flight crew scheduling problems.
- [Machine reassignment (Google ROADEF 2012)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/machine-reassignment/machine-reassignment.html): Documents the Google ROADEF 2012 Machine Reassignment problem, detailing its constraints, objectives, domain model, and example problem sizes for process-to-machine assignment optimization.
- [Meeting scheduling](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/meeting-scheduling/meeting-scheduling.html): Explains the meeting scheduling optimization problem, detailing hard, medium, and soft constraints for assigning meetings to rooms and times without conflicts.
- [N queens](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/nqueens/nqueens.html): Explains the N queens puzzle, its constraints, domain model, and solution approach, using four queens as a primary example and Java code snippets for illustration.
- [Nurse rostering (INRC 2010)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/nurse-rostering/nurse-rostering.html): Explains the nurse rostering problem as defined by the International Nurse Rostering Competition 2010, detailing hard and soft constraints, dataset sizes, and domain concepts.
- [Project job scheduling](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/project-job-scheduling/project-job-scheduling.html): Describes the project job scheduling problem, where jobs with multiple execution modes are assigned to minimize project delays while respecting resource and precedence constraints.
- [Task assigning](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/task-assigning/task-assigning.html): Describes the task assigning optimization problem, including constraints, objectives, and domain model for assigning tasks to employees based on skills, priorities, and fairness.
- [Tennis club scheduling](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/tennis-scheduling/tennis-scheduling.html): Describes how to schedule weekly round robin matches for a tennis club with four teams, considering team availability, fairness, and balanced confrontations.
- [Traveling salesman (TSP - traveling salesman problem)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/travelling-salesman/travelling-salesman.html): Explains the traveling salesman problem (TSP), its formal definition, real-world relevance, problem size examples, and the computational difficulty of finding optimal solutions.
- [Traveling tournament problem (TTP)](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/travelling-tournament/travelling-tournament.html): Explains the traveling tournament problem (TTP), detailing its scheduling constraints, optimization objective, and example problem sizes for sports league match planning.
- [Vehicle routing](https://docs.timefold.ai/timefold-solver/0.8.x/use-cases-and-examples/vehicle-routing/vehicle-routing.html): Describes the vehicle routing problem (CVRP and CVRPTW), including constraints, problem size, and practical considerations for using road distances and integrating with real-world map data.
- [Platform concepts](https://docs.timefold.ai/timefold-platform/latest/concepts.html): Explains the core concepts of the Timefold Platform, including its architecture, tenants, members, models, and datasets, and how these elements interact within the platform.
- [Feature requests](https://docs.timefold.ai/timefold-platform/latest/feature-requests.html): Explains how to submit, track, and manage feature requests for the project, including guidelines for proposing new features and interacting with the development team.
- [Getting started with the Timefold Platform](https://docs.timefold.ai/timefold-platform/latest/getting-started-with-the-timefold-platform.html): This page explains how to get started with the Timefold Platform, including exploring models, starting a trial, using demo data, inviting team members, and accessing model APIs.
- [Introduction](https://docs.timefold.ai/timefold-platform/latest/introduction.html): The Introduction page explains the core features and benefits of the Timefold Platform, including its REST API, enterprise-ready models, scalability, and developer tools for planning solutions.
- [Scheduling API concepts](https://docs.timefold.ai/timefold-platform/latest/planning-ai-concepts.html): Introduces core Planning AI concepts with examples from field service routing and employee shift scheduling, linking to detailed guides for each model.
- [Pricing plans and capabilities](https://docs.timefold.ai/timefold-platform/latest/plans.html)
- [API integration](https://docs.timefold.ai/timefold-platform/latest/api/api-integration.html): Learn how to integrate with Timefold APIs.
- [Handling changes to your planning data](https://docs.timefold.ai/timefold-platform/latest/api/handling-changes-to-your-planning-data.html): Learn how to handle changes to your planning data when integrating with the Timefold API.
- [Model API usage](https://docs.timefold.ai/timefold-platform/latest/api/model-api-usage.html): Learn how to use the model APIs and configure API keys.
- [Platform API usage](https://docs.timefold.ai/timefold-platform/latest/api/platform-api.html): Learn how to use the Timefold Platform API to automate or integrate with platform administration using Personal Access Tokens.
- [Balancing different optimization goals](https://docs.timefold.ai/timefold-platform/latest/guides/balancing-different-optimization-goals.html): Explains how Timefold Platform enables users to balance multiple optimization goals in planning problems by configuring constraint weights, interpreting scores, and aligning plans with business priorities.
- [Designing better routing plans with (just enough) traffic awareness](https://docs.timefold.ai/timefold-platform/latest/guides/designing-routing-plans-with-traffic-awareness.html): This page explains how to design routing plans that incorporate traffic awareness, detailing types of traffic data, their impact on routing, scalability challenges, and practical strategies for effective, resilient route optimization.
- [Job-oriented guides](https://docs.timefold.ai/timefold-platform/latest/guides/job-oriented-guides.html): Job-oriented guides connect real-world planning and scheduling challenges to Timefold optimization concepts, modeling strategies, and platform features through practical, scenario-driven walkthroughs.
- [Responding to disruptions with real-time planning](https://docs.timefold.ai/timefold-platform/latest/guides/responding-to-disruptions-with-real-time-replanning.html): This page explains how Timefold enables operational planners to adapt to real-world disruptions by supporting real-time replanning, impact analysis, and flexible workflows for handling unexpected events.
- [Uncovering inefficiencies in operational planning](https://docs.timefold.ai/timefold-platform/latest/guides/uncovering-inefficiencies-in-operational-planning.html): This page explains how to identify and address bottlenecks and inefficiencies in operational planning using Timefold’s optimization features, metrics, and upcoming analysis tools.
- [Validating an optimized plan with Explainable AI](https://docs.timefold.ai/timefold-platform/latest/guides/validating-an-optimized-plan-with-explainable-ai.html): Explains how Timefold’s explainable AI features help planners validate, understand, and trust optimized plans by providing visualizations, real-world metrics, constraint justifications, and transparent decision logic.
- [Reviewing the audit log](https://docs.timefold.ai/timefold-platform/latest/how-tos/audit-log.html): Explains how to access, review, and filter the Timefold Platform audit log to track configuration events, changes, and user actions for each tenant.
- [Searching and categorizing datasets for auditability](https://docs.timefold.ai/timefold-platform/latest/how-tos/categorizing-runs.html): Explains how to organize, search, and filter datasets using names, tags, and multiple criteria in the Plans Overview for improved auditability and analysis.
- [Comparing datasets (preview)](https://docs.timefold.ai/timefold-platform/latest/how-tos/comparing-runs.html): The Comparing datasets (preview) page explains how to use the Timefold Platform's Comparison UI to assess, compare, and analyze multiple datasets, including metrics, configuration differences, and saved comparisons.
- [Configuration parameters and profiles](https://docs.timefold.ai/timefold-platform/latest/how-tos/configuration-parameters-and-profiles.html): Explains how to define, manage, and use configuration parameters and profiles in Timefold Platform, including overrides, constraint weights, goal alignment, and benchmarking strategies.
- [Timefold Copilot (preview)](https://docs.timefold.ai/timefold-platform/latest/how-tos/copilot.html)
- [Real-time planning with /from-patch (preview)](https://docs.timefold.ai/timefold-platform/latest/how-tos/from-patch-endpoint.html): Explains how to use the /from-patch API endpoint to incrementally update planning datasets in real time, including payload format, validation, traceability, and versioning.
- [Insights (preview)](https://docs.timefold.ai/timefold-platform/latest/how-tos/insights.html)
- [Interpreting dataset results](https://docs.timefold.ai/timefold-platform/latest/how-tos/interpreting-model-run-results.html): Explains how to interpret dataset results in Timefold Platform, including understanding metrics, scores, constraint analysis, and available API endpoints for accessing solution details.
- [Using the maps service](https://docs.timefold.ai/timefold-platform/latest/how-tos/maps-service.html): This page explains how to use the maps service, configure map providers, implement external providers, and manage location sets and distance matrices via the platform APIs.
- [Member management and roles](https://docs.timefold.ai/timefold-platform/latest/how-tos/member-management-and-roles.html): Explains how to manage tenant members in Timefold, including inviting users, assigning roles (User or Administrator), and setting membership expiration options.
- [Dataset lifecycle](https://docs.timefold.ai/timefold-platform/latest/how-tos/run-lifecycle.html): Explains the statuses and lifecycle of a dataset during solving, including submission options, status transitions, cancellation, restarts, failure handling, and webhook event integration.
- [Secrets management](https://docs.timefold.ai/timefold-platform/latest/how-tos/secrets-management.html): Explains how tenant admins can securely create, manage, and use encrypted secrets for API keys or tokens in Timefold Platform integrations like webhooks and external map providers.
- [Solve queue](https://docs.timefold.ai/timefold-platform/latest/how-tos/solve-queue.html): The Solve queue feature manages per-tenant queues for optimization workloads, automatically scheduling and prioritizing solve operations when concurrency limits are reached, with UI and API visibility and admin controls.
- [Installation instructions](https://docs.timefold.ai/timefold-platform/latest/installation-docs/index.html): This page provides step-by-step instructions and requirements for self-hosting the Timefold Platform on Kubernetes, including prerequisites, supported environments, configuration, and uninstallation.
- [Model catalog and documentation](https://docs.timefold.ai/timefold-platform/latest/models/catalog.html): Describes the available documentation, demo data, versioning, and maturity levels for models in the Timefold Platform model catalog.
- [Trialing Timefold models](https://docs.timefold.ai/timefold-platform/latest/models/trials.html): Explains how to sign up for a Timefold Platform trial, access and test available models with demo datasets, and outlines trial limitations and support options.
- [Model maturity and versioning](https://docs.timefold.ai/timefold-platform/latest/models/versioning-and-maturity.html): This page details the maturity levels and semantic versioning system for Timefold models, explains how model and platform versions are managed and tracked, and describes how version visibility and documentation work within the Timefold Platform.
- [Self-Hosted vs. Timefold Cloud Platform](https://docs.timefold.ai/timefold-platform/latest/self-hosted/self-hosted-vs-cloud.html): Explains the differences, benefits, and limitations of using the managed Timefold Cloud Platform versus deploying and operating a self-hosted Timefold Platform instance.
- [Troubleshooting](https://docs.timefold.ai/timefold-platform/latest/self-hosted/troubleshoot.html): Describes how to troubleshoot issues in self-hosted Timefold Platform installations by collecting run data, using provided scripts and API calls, and verifying platform and model versions.
- [Upgrade instructions](https://docs.timefold.ai/timefold-platform/latest/self-hosted/upgrading.html): Step-by-step instructions for upgrading an existing Timefold Platform installation, including Helm commands, version-specific changes, and required configuration updates for Kubernetes environments.
- [Contacting support](https://docs.timefold.ai/timefold-platform/latest/support/contacting-support.html): Explains how to contact the Timefold support team by email for help with questions or issues related to the Timefold Platform.
- [Platform status](https://docs.timefold.ai/timefold-platform/latest/support/status-page.html): Describes how to check the real-time operational status, ongoing incidents, and scheduled maintenance for the Timefold platform at https://status.timefold.ai/.
- [Troubleshooting](https://docs.timefold.ai/timefold-platform/latest/support/troubleshooting.html): This page provides step-by-step instructions for diagnosing and resolving connectivity issues with the Timefold Platform, including DNS, network, and browser troubleshooting.
- [AI legislation compliance](https://docs.timefold.ai/timefold-platform/latest/trust/ai-compliance.html): Explains how Timefold's deterministic, rule-based optimization technology aligns with the European Union AI Act and related AI legislation, emphasizing transparency, explainability, and compliance considerations.
- [Data security](https://docs.timefold.ai/timefold-platform/latest/trust/data-security.html): This page details the data security measures in the Timefold Cloud Platform, including access monitoring, encryption, backups, network protections, vulnerability management, and tenant data retention policies.
- [Legal and privacy](https://docs.timefold.ai/timefold-platform/latest/trust/legal-and-privacy.html): Summarizes Timefold’s legal and privacy policies, including terms of use, privacy policy, data processing agreement, service-level agreement, and cookie policy, with links to official documents.
- [Product security](https://docs.timefold.ai/timefold-platform/latest/trust/product-security.html): Describes the security features of the Timefold Cloud Platform, including audit logging, user role management, authentication policies, webhook integrations, data retention, and email security measures.
- [Risk profile](https://docs.timefold.ai/timefold-platform/latest/trust/risk-profile.html): Documents the risk profile of the Timefold Cloud Platform, including recovery objectives, hosting details, third-party dependencies, data privacy practices, and compliance certifications.
- [Air-gapped environments](https://docs.timefold.ai/timefold-platform/latest/api/integration-scenarios/air-gapped.html)
- [Data residency requirements](https://docs.timefold.ai/timefold-platform/latest/api/integration-scenarios/data-residency.html)
- [Integration scenarios](https://docs.timefold.ai/timefold-platform/latest/api/integration-scenarios/integration-scenarios.html)
- [Multiple environments and clusters](https://docs.timefold.ai/timefold-platform/latest/api/integration-scenarios/multi-environment.html)
- [Polling](https://docs.timefold.ai/timefold-platform/latest/api/receiving-model-api-results/polling.html): Learn about polling Timefold for dataset solutions.
- [Receiving model API results](https://docs.timefold.ai/timefold-platform/latest/api/receiving-model-api-results/receiving-model-api-results.html): Learn how to integrate with Timefold APIs.
- [Server sent events (SSE)](https://docs.timefold.ai/timefold-platform/latest/api/receiving-model-api-results/server-sent-events.html): Learn about server sent events for retrieving dataset solutions.
- [Webhooks](https://docs.timefold.ai/timefold-platform/latest/api/receiving-model-api-results/webhooks.html): Learn how to integrate in near real-time via webhooks
- [Changelog](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/changelog.html): Changelog for Timefold's Employee Shift Scheduling model.
- [Feature requests](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/feature-requests.html)
- [Getting started: Hello world](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/getting-started-with-employee-scheduling.html): A hello world example for Timefold's Employee Shift Scheduling model
- [Introduction](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/introduction.html): An introduction to Timefold's Employee Shift Scheduling model.
- [Manual intervention](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/manual-intervention.html): An introduction to manual intervention with employee shift scheduling.
- [Real-time planning with patches](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/real-time-planning-preview.html): An introduction to real-time planning for employee shift scheduling.
- [Real-time planning](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/real-time-planning.html): An introduction to real-time planning for employee shift scheduling.
- [Recommendations](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/recommendations.html): Learn how to get recommendations for the best employee to cover a schedule in employee shift scheduling.
- [Upgrade to the latest version](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/upgrade-to-the-latest-version.html): How to upgrade between versions of the Employee Shift Scheduling model.
- [Employee activation](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-activation.html): Learn about minimizing employee activation costs in employee shift scheduling.
- [Employee contracts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-contracts.html): Learn about employee contracts in employee shift scheduling.
- [Employee priority](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-priority.html): Learn about employee priority in employee shift scheduling.
- [Employee resource constraints](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-resource-constraints.html): Learn about employee resource constraints in employee shift scheduling.
- [Pairing employees](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/pairing-employees.html): Learn about pairing employees in employee shift scheduling.
- [Shift Breaks](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-breaks.html): Learn about defining breaks during shifts with employee shift scheduling.
- [Shift travel and locations](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-travel-and-locations.html): Learn about limiting employee travel for shifts and the location of shifts in employee shift scheduling.
- [Configuring employee well-being](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/scenarios/configuring-employee-wellbeing.html)
- [Configuring labor law compliance](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/scenarios/configuring-labor-law-compliance.html)
- [Scenarios](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/scenarios/scenarios.html): Learn about different scenarios in employee shift scheduling.
- [Self-rostering and optimization](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/scenarios/self-rostering-and-optimization.html)
- [Alternative shifts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/alternative-shifts.html): Learn about alternative shifts in employee shift scheduling.
- [Demand-based scheduling](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/demand-based-scheduling.html): Learn about meeting the hourly demand for shifts in employee shift scheduling with demand-based scheduling.
- [Mandatory and optional shifts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/mandatory-and-optional-shifts.html): Learn about resource-limited planning and mandatory and optional shifts in employee shift scheduling.
- [Shift service constraints](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/shift-service-constraints.html): Learn about shift service constraints in employee shift scheduling.
- [Skills and risk factors](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/skills-and-risk-factors.html): Learn about skills and risk factors in employee shift scheduling.
- [Constraints](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/constraints.html): Learn about the hard, medium, and soft constraints in Timefold's Employee Shift Scheduling model
- [Demo datasets](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/demo-datasets.html): Learn how to use the demo datasets in Timefold Platform.
- [Input validation](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/input-validation.html): Learn about input dataset validation in employee shift scheduling.
- [Integration](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/integration.html): Learn about Timefold integrating with your current systems.
- [Metrics and optimization goals](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/metrics-and-optimization-goals.html): An introduction to metrics of the Employee Shifts Scheduling model and its optimization goals.
- [Scheduling API concepts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/planning-ai-concepts.html): An introduction to Scheduling API concepts.
- [Score analysis](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/score-analysis.html): Understand the score structure of your employee shift schedules.
- [Tags and tag types](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/tags-and-tag-types.html): Learn about tags and tag types in employee shift scheduling.
- [Terminology](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/terminology.html): Learn the terminology used in employee shift scheduling.
- [Time zones and Daylight Saving Time (DST)](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/time-zones-and-dst.html): Learn about working with multiple time zones and daylight-saving time in Timefold's Employee Shift Scheduling model.
- [Use case guide](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/use-cases.html)
- [User guide](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/user-guide.html): Learn about the Employee Shift Scheduling model.
- [Visualizations](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/visualizations.html)
- [Employee availability and preferences](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-availability-and-preferences/employee-availability-and-preferences.html): Learn about employee availability and preferences in employee shift scheduling.
- [Employee availability](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-availability-and-preferences/employee-availability.html): Learn about employee availability in employee shift scheduling.
- [Employee preferences](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/employee-availability-and-preferences/employee-preferences.html): Learn about employee preferences in employee shift scheduling.
- [Balance shift count](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/fairness/balance-shift-count.html): Learn about fairness with balance shift count rules in employee shift scheduling.
- [Balance time worked](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/fairness/balance-time-worked.html): Learn about fairness with balance time worked rules in employee shift scheduling.
- [Fairness](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/fairness/fairness.html): Learn about fairness in employee shift scheduling.
- [Daily shift pairings](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/daily-shift-pairings.html): Learn about pairing shifts together in employee shift scheduling.
- [Minimize gaps between shifts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/minimize-gaps-between-shifts.html): Learn about minimizing gaps between shifts in employee shift scheduling.
- [Minutes between shifts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/minutes-between-shifts.html): Learn about how to manage minutes between shifts in employee shift scheduling.
- [Multi-day shift sequence patterns](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/multi-day-shift-sequence-patterns.html): Learn about multi-day shift sequence patterns in employee shift scheduling.
- [Overlapping shifts](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/overlapping-shifts.html): Learn about overlapping shifts in employee shift scheduling.
- [Shift rotations and patterns](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/shift-rotation-and-patterns.html): Learn about shift rotations and patterns in employee shift scheduling.
- [Shift rotations](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/shift-rotations.html): Learn about shift rotations in employee shift scheduling.
- [Shift start times differences](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/shift-start-time-differences.html): Learn about minimizing disruptions for employee shift start times in employee shift scheduling.
- [Single day shift sequence patterns](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-rotations-and-patterns/single-day-shift-sequence-patterns.html): Learn about single day shift sequence patterns in employee shift scheduling.
- [Shift tag types](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-type-diversity/shift-tag-types.html): Learn about shift tag types in employee shift scheduling.
- [Shift type diversity](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-type-diversity/shift-type-diversity.html): Learn about shift type diversity in employee shift scheduling.
- [Shift types worked per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-type-diversity/shift-types-worked-per-period.html): Learn about managing the number of shift types employees work in employee shift scheduling.
- [Unique tags per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/shift-type-diversity/unique-tags-per-period.html): Learn about managing the variety of tasks employees can be assigned with unique tags in employee shift scheduling.
- [Shifts to avoid close to day off requests](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/avoid-shifts-close-to-day-off-requests.html): Learn about avoiding assigning certain shifts close to day off requests in employee shift scheduling.
- [Consecutive days off in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/consecutive-days-off-in-rolling-window.html): Learn about managing consecutive days off in rolling windows for employees in employee shift scheduling.
- [Consecutive days off per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/consecutive-days-off-per-period.html): Learn about managing consecutive days off per period for employees in employee shift scheduling.
- [Consecutive minutes off in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/consecutive-minutes-off-in-rolling-window.html): Learn about managing time off in minutes in rolling windows for employees in employee shift scheduling.
- [Consecutive weekends off per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/consecutive-weekends-off-per-period.html): Learn about managing consecutive weekends off per period for employees in employee shift scheduling.
- [Days off per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/days-off-per-period.html): Learn about managing days per period for employees in employee shift scheduling.
- [Time off](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/time-off/time-off.html): Learn about managing employee's time off in employee shift scheduling.
- [Consecutive days worked and shift sequences](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/consecutive-days-worked-and-shift-sequences.html): Learn about managing employees' hours with consecutive days worked and shift sequences in employee shift scheduling.
- [Consecutive days worked](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/consecutive-days-worked.html): Learn about managing employees' hours with consecutive days worked rules in employee shift scheduling.
- [Consecutive shifts worked](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/consecutive-shifts-worked.html): Learn about managing employees' hours with consecutive shifts worked rules in employee shift scheduling.
- [Consecutive weekends worked](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/consecutive-weekends-worked.html): Learn about managing employee hours with consecutive weekends worked in employee shift scheduling.
- [Days worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/days-worked-in-a-rolling-window.html): Learn about managing employee hours with days worked in a rolling window in employee shift scheduling.
- [Days worked per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/days-worked-per-period.html): Learn about managing employees' hours with days worked per period in employee shift scheduling.
- [Minutes logged per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/minutes-logged-per-period.html): Learn about the difference between shift duration and time logged in employee shift scheduling.
- [Minutes worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/minutes-worked-in-a-rolling-window.html): Learn about managing employee hours with minutes worked in a rolling window in employee shift scheduling.
- [Minutes worked per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/minutes-worked-per-period.html): Learn about managing employee hours with minutes worked per period in employee shift scheduling.
- [Shifts worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/shifts-worked-in-a-rolling-window.html): Learn about managing employee hours with shifts worked in a rolling window in employee shift scheduling.
- [Shifts worked per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/shifts-worked-per-period.html): Learn about managing employees' hours with shifts worked per period in employee shift scheduling.
- [Weekend minutes worked per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/weekend-minutes-worked-per-period.html): Learn about managing employee hours with weekend minutes worked per period in employee shift scheduling.
- [Weekends worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/weekends-worked-in-a-rolling-window.html): Learn about managing employee hours with weekends worked in a rolling window in employee shift scheduling.
- [Weekends worked per period](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/weekends-worked-per-period.html): Learn about managing employee hours with weekends worked per period in employee shift scheduling.
- [Work limits](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/employee-resource-constraints/work-limits/work-limits.html): Learn about managing employee's work time in employee shift scheduling.
- [Cost groups](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/cost-management/cost-groups.html): Learn about managing costs in employee shift scheduling with cost groups.
- [Cost management](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/cost-management/cost-management.html): Learn about managing costs in employee shift scheduling.
- [Employee rates](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/cost-management/employee-rates.html): Learn about managing costs in employee shift scheduling with employee rates.
- [Employee selection](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/shift-assignments/employee-selection.html): Learn about choosing the right employee for shift assignments in employee shift scheduling.
- [Shift assignments](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/shift-assignments/shift-assignments.html): Learn about shift assignments in employee shift scheduling.
- [Shift selection](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/shift-service-constraints/shift-assignments/shift-selection.html): Learn about selecting the right number of shifts for shift assignments in employee shift scheduling.
- [Input datasets](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/input-datasets/input-datasets.html): Learn about the input datasets used with Timefold's Employee Shift Scheduling model API
- [Model configuration](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/input-datasets/model-configuration.html): Learn about the model configuration in Timefold's Employee Shift Scheduling model
- [Model input](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/input-datasets/model-input.html): Learn about the model input in datasets used with Timefold's Employee Shift Scheduling model API
- [Planning window](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/input-datasets/planning-window.html): Learn about planning windows in employee shift scheduling.
- [Input metrics](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/output-datasets/input-metrics.html): Learn about the input metrics collected in the output datasets used with Timefold's Employee Shift Scheduling model API
- [Key performance indicators (KPIs)](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/output-datasets/key-performance-indicators.html): Learn about the KPIs include in output datasets used with Timefold's Employee Shift Scheduling model API
- [Metadata](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/output-datasets/metadata.html): Learn about the metadata included in output datasets used with Timefold's Employee Shift Scheduling model API
- [Model output](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/output-datasets/model-output.html): Learn about the model output included in output datasets used with Timefold's Employee Shift Scheduling model API
- [Output datasets](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/output-datasets/output-datasets.html): Learn about the output datasets used with Timefold's Employee Shift Scheduling model API
- [API tooling](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/using-the-api/api-tooling.html): Use the Employee Shift Scheduling OpenAPI spec to generate API clients, import Postman collections, or create Bruno collections.
- [Using the API](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/using-the-api/using-the-api.html): Explore the Employee Shift Scheduling API reference and learn how to use API tooling.
- [Using the OpenAPI spec](https://docs.timefold.ai/employee-shift-scheduling/1.28.x/user-guide/using-the-api/using-the-openapi-spec.html): Understand how to use and explore the Employee Shift Scheduling OpenAPI specification.
- [Changelog](https://docs.timefold.ai/employee-shift-scheduling/latest/changelog.html): The changelog documents recent updates and new features for Timefold's Employee Shift Scheduling model, including enhancements to shift tag expressions, employee priorities, demand intervals, and disruption rules.
- [Feature requests](https://docs.timefold.ai/employee-shift-scheduling/latest/feature-requests.html): Explains how to submit, track, and manage feature requests for the Timefold platform, including guidelines for users and the review process.
- [Getting started: Hello world](https://docs.timefold.ai/employee-shift-scheduling/latest/getting-started-with-employee-scheduling.html): This page demonstrates how to create, submit, and retrieve results for a basic Employee Shift Scheduling dataset using the Timefold API, including required input structure and example API calls.
- [Introduction](https://docs.timefold.ai/employee-shift-scheduling/latest/introduction.html): The Introduction page explains Timefold's Employee Shift Scheduling model, its core constraints, integration options, APIs, and guidance for developers, architects, and business decision makers.
- [Manual intervention](https://docs.timefold.ai/employee-shift-scheduling/latest/manual-intervention.html): Explains how to manually assign and pin employee shifts in scheduling, including use cases like solution comparison, gradual adoption, real-time planning, self-rostering, and experiments.
- [Real-time planning with patches](https://docs.timefold.ai/employee-shift-scheduling/latest/real-time-planning-preview.html): Explains how to use real-time planning in employee shift scheduling to handle last-minute changes, such as illness or increased demand, with concrete API examples and patch operations.
- [Real-time planning](https://docs.timefold.ai/employee-shift-scheduling/latest/real-time-planning.html): Explains how to use real-time planning to update employee shift schedules in response to illness, increased demand, or on-call requirements, with concrete JSON and API examples.
- [Recommendations](https://docs.timefold.ai/employee-shift-scheduling/latest/recommendations.html): Describes how to use the Timefold recommendations API to select the best employees to cover shifts, including input dataset structure, API usage, and handling employee preferences.
- [Upgrade to the latest version](https://docs.timefold.ai/employee-shift-scheduling/latest/upgrade-to-the-latest-version.html): Describes how to upgrade between versions of the Employee Shift Scheduling model, detailing breaking changes, deprecated fields, and required migration steps for each release.
- [Employee activation](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-activation.html): Describes how to configure and use employee activation constraints in shift scheduling to minimize activation costs, maximize employee utilization, and maintain activation ratios between employee groups.
- [Employee contracts](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-contracts.html): Describes how to define, assign, and prioritize employee contracts in shift scheduling, including contract rules, priorities, and period-based validity for different employee groups.
- [Employee priority](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-priority.html): Explains how employee priority levels affect shift assignment, constraint satisfaction, and preference handling in employee shift scheduling, including examples and impacted constraints.
- [Employee resource constraints](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-resource-constraints.html): Describes the various types of employee resource constraints in shift scheduling, including work limits, time off, shift rotations, shift type diversity, and fairness considerations.
- [Pairing employees](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/pairing-employees.html): Explains how to define, configure, and filter required, preferred, unpreferred, and prohibited employee pairings for shift scheduling, including examples using employee IDs and tags.
- [Shift Breaks](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-breaks.html): Explains how to define, validate, and use breaks within employee shifts, including tagging breaks for working time calculations and handling multi-day shifts with breaks.
- [Shift travel and locations](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-travel-and-locations.html): Explains how to configure and enforce employee shift assignments with travel distance limits, minimum travel time between shifts, and location-based scheduling constraints using contract rules.
- [Configuring employee well-being](https://docs.timefold.ai/employee-shift-scheduling/latest/scenarios/configuring-employee-wellbeing.html): Explains how to configure and monitor employee well-being constraints and supporting tools in Timefold’s Employee Shift Scheduling model to promote fairness, rest, and sustainable schedules.
- [Configuring labor law compliance](https://docs.timefold.ai/employee-shift-scheduling/latest/scenarios/configuring-labor-law-compliance.html): This page explains how to configure Timefold’s Employee Shift Scheduling model to represent common labor law requirements for working hours, rest periods, overtime, and region-specific rules.
- [Scenarios](https://docs.timefold.ai/employee-shift-scheduling/latest/scenarios/scenarios.html): Describes various scenarios encountered in employee shift scheduling, outlining different use cases and approaches relevant to workforce planning.
- [Self-rostering and optimization](https://docs.timefold.ai/employee-shift-scheduling/latest/scenarios/self-rostering-and-optimization.html)
- [Alternative shifts](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/alternative-shifts.html): Describes how to configure and assign alternative shifts in employee scheduling by defining shift groups, applying assignment rules, and using tags to control rule application.
- [Demand-based scheduling](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/demand-based-scheduling.html): Explains how to configure and use demand-based scheduling in employee shift planning, including defining hourly demand intervals, filtering by tags, and balancing shifts to meet varying workforce requirements.
- [Mandatory and optional shifts](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/mandatory-and-optional-shifts.html): This page explains how to configure and use mandatory and optional shifts, shift priorities, and resource-limited planning in employee shift scheduling with practical examples.
- [Shift service constraints](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/shift-service-constraints.html): Explains the various aspects of shift service constraints in employee shift scheduling, focusing on how shifts are managed and organized within a scheduling system.
- [Skills and risk factors](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/skills-and-risk-factors.html): Explains how to use skills, skill matching strategies, and risk factors to assign employees to shifts in employee scheduling, with practical JSON examples and API usage.
- [Constraints](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/constraints.html): Explains the hard, medium, and soft constraints used in Timefold's Employee Shift Scheduling model, including examples, penalty calculations, and constraint weight configuration.
- [Demo datasets](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/demo-datasets.html): Explains how to access, download, and use the employee shift scheduling demo datasets in Timefold Platform through both the UI and API.
- [Input validation](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/input-validation.html): Explains input dataset validation for employee shift scheduling, detailing validation rules, common errors, warnings, and their solutions to ensure correct schedule configuration.
- [Integration](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/integration.html): Explains how to connect and integrate Timefold with your existing systems, covering supported integration methods, workflows, and configuration steps.
- [Metrics and optimization goals](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/metrics-and-optimization-goals.html): Explains the key metrics used in Employee Shift Scheduling and how these metrics relate to defining and achieving optimization goals within Timefold.
- [Scheduling API concepts](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/planning-ai-concepts.html): Introduces core concepts of Planning AI, including how optimization models are used to solve real-world scheduling and resource allocation problems.
- [Score analysis](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/score-analysis.html): Explains how to use the score analysis feature and API endpoints to view constraint breakdowns and justifications for employee shift schedules.
- [Tags and tag types](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/tags-and-tag-types.html): Explains how tags and tag types are used in employee shift scheduling to filter employees or shifts in rules, including conventions for inclusion and exclusion.
- [Terminology](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/terminology.html): Defines key terms used in employee shift scheduling, including constraints, contracts, datasets, employees, planning windows, shifts, shift groups, and shift patterns.
- [Time zones and Daylight Saving Time (DST)](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/time-zones-and-dst.html): Explains how the Employee Shift Scheduling model handles multiple time zones and Daylight Saving Time, including shift definitions, employee time zones, and rule behavior across offsets.
- [Use case guide](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/use-cases.html): This page explains how to apply the Employee Shift Scheduling model to real-world use cases in healthcare, retail, security, and hospitality, detailing common challenges and recommended configuration strategies.
- [User guide](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/user-guide.html): The User guide introduces the Employee Shift Scheduling model and outlines the main topics covered for understanding and using this feature.
- [Visualizations](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/visualizations.html): Explains how to use the Employee Shift Scheduling model’s four visualization views to validate schedules, detect coverage gaps, analyze employee utilization, and uncover operational inefficiencies.
- [Employee availability and preferences](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-availability-and-preferences/employee-availability-and-preferences.html): Learn about employee availability and preferences in employee shift scheduling.
- [Employee availability](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-availability-and-preferences/employee-availability.html): Learn about employee availability in employee shift scheduling.
- [Employee preferences](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/employee-availability-and-preferences/employee-preferences.html): Learn about employee preferences in employee shift scheduling.
- [Balance shift count](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/fairness/balance-shift-count.html): Explains how to configure and use balance shift count rules to ensure fair distribution of shifts among employees in Timefold employee scheduling.
- [Balance time worked](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/fairness/balance-time-worked.html): Explains how to configure and use balance time worked rules to ensure fair distribution of working hours among employees in shift scheduling, including FTE adjustments and historical data.
- [Fairness](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/fairness/fairness.html): Explains how to achieve fairness in employee shift scheduling by balancing workload through time worked and shift count rules to improve satisfaction and retention.
- [Daily shift pairings](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/daily-shift-pairings.html): Describes how to configure and use daily shift pairing rules in employee scheduling, including required, preferred, and prohibited pairings with practical JSON examples and constraint behaviors.
- [Minimize gaps between shifts](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/minimize-gaps-between-shifts.html): Explains how to configure and use the minimize gaps between shifts constraint to encourage assigning employee split shifts as close together as possible within a specified time window.
- [Minutes between shifts](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/minutes-between-shifts.html): Explains how to configure and enforce minimum and maximum minutes between employee shifts using minutesBetweenShiftsRules in contracts, including rule attributes, tag filtering, and hard or soft constraint behavior.
- [Multi-day shift sequence patterns](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/multi-day-shift-sequence-patterns.html): Explains how to configure, apply, and prioritize multi-day shift sequence pattern rules in employee scheduling, including JSON examples for preferred, prohibited, and optional shift assignments.
- [Overlapping shifts](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/overlapping-shifts.html): Explains how to configure and manage overlapping employee shifts using contract rules, including API examples for allowing specific shift tag overlaps in scheduling solutions.
- [Shift rotations and patterns](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/shift-rotation-and-patterns.html): Explains how to manage employee shift rotations and patterns, including alternating schedules, split shifts, multi-day sequences, shift pairing, overlaps, and minimizing start time variation.
- [Shift rotations](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/shift-rotations.html): Describes how to configure and use shift rotation rules in employee shift scheduling, including required and preferred weekly or custom rotation periods, rule structure, and practical examples.
- [Shift start times differences](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/shift-start-time-differences.html): Explains how to configure and enforce limits on the difference between employee shift start times using the shiftStartTimeDifferenceInMinutesMax rule, including required and preferred constraint modes.
- [Single day shift sequence patterns](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-rotations-and-patterns/single-day-shift-sequence-patterns.html): Explains how to configure and use single day shift sequence pattern rules in employee scheduling to prefer, avoid, or prohibit specific combinations of shifts within a single day.
- [Shift tag types](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-type-diversity/shift-tag-types.html): Explains how to use shift tag types, required and preferred tag rules, and tag expressions to assign employees to shifts based on tags and organizational structure.
- [Shift type diversity](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-type-diversity/shift-type-diversity.html): Explains how to manage shift type diversity in employee shift scheduling by considering contractual obligations and providing examples for honoring employee contract terms.
- [Shift types worked per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-type-diversity/shift-types-worked-per-period.html): Explains how to configure and enforce rules limiting the number of different shift types an employee can work per period in employee shift scheduling, including required and preferred constraints.
- [Unique tags per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/shift-type-diversity/unique-tags-per-period.html): Explains how to use the uniqueTagsLimit rule in employee shift scheduling to restrict the number of different task tags assigned to each employee within a period.
- [Shifts to avoid close to day off requests](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/avoid-shifts-close-to-day-off-requests.html): Explains how to configure rules that prevent or discourage assigning specific shifts to employees immediately before or after their requested days off in shift scheduling.
- [Consecutive days off in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/consecutive-days-off-in-rolling-window.html): Explains how to configure and enforce rules for consecutive days off in a rolling window for employee shift scheduling, including required, preferred, and conditional constraints with practical JSON examples.
- [Consecutive days off per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/consecutive-days-off-per-period.html): Explains how to configure and enforce minimum and maximum consecutive days off per period for employees in shift scheduling, including required and preferred rule settings, API examples, and constraint behavior.
- [Consecutive minutes off in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/consecutive-minutes-off-in-rolling-window.html): Explains how to configure and enforce rules for consecutive minutes off in a rolling window for employee shift scheduling, including required and preferred constraints, tagging, and conditional logic.
- [Consecutive weekends off per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/consecutive-weekends-off-per-period.html): Explains how to configure and enforce rules for the minimum number of consecutive weekends off per period in employee shift scheduling, including required and preferred constraints.
- [Days off per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/days-off-per-period.html): Explains how to configure and enforce minimum and maximum days off per period for employees in shift scheduling using contract period rules, including required and preferred constraints.
- [Time off](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/time-off/time-off.html): Explains how to manage employee time off in shift scheduling, including rules for days off, consecutive days, weekends, rolling windows, and handling day off requests.
- [Consecutive days worked and shift sequences](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/consecutive-days-worked-and-shift-sequences.html): Explains how to configure and enforce rules for consecutive days worked and shift sequences, including minimum time between sequences, allowed start days, and compact sequence incentives in employee scheduling.
- [Consecutive days worked](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/consecutive-days-worked.html): Explains how to configure and use consecutive days worked rules in employee shift scheduling, including required and preferred limits, tags, and example API payloads.
- [Consecutive shifts worked](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/consecutive-shifts-worked.html): Learn about managing employees' hours with consecutive shifts worked rules in employee shift scheduling.
- [Consecutive weekends worked](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/consecutive-weekends-worked.html): Explains how to configure and enforce rules for limiting the number of consecutive weekends employees work using period rules in employee shift scheduling.
- [Days worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/days-worked-in-a-rolling-window.html): Explains how to configure and use days worked in a rolling window rules to limit employee shifts, including required, preferred, and conditional constraints with practical JSON examples.
- [Days worked per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/days-worked-per-period.html): Explains how to configure and enforce rules for the maximum and minimum number of days employees can work per period in shift scheduling, including required and preferred constraints.
- [Minutes logged per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/minutes-logged-per-period.html): Explains how to configure and enforce rules for the maximum and minimum minutes employees can log per period in shift scheduling, including required and preferred constraints.
- [Minutes worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/minutes-worked-in-a-rolling-window.html): Explains how to configure and enforce employee work limits using minutes worked in a rolling window, including rule definitions, tag filtering, time intervals, and conditional constraints.
- [Minutes worked per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/minutes-worked-per-period.html): Explains how to configure and enforce employee work limits using minutes worked per period rules, including required and preferred constraints, shift tag filtering, and time interval counting.
- [Shifts worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/shifts-worked-in-a-rolling-window.html): Explains how to configure and use rolling window rules to limit or prefer the number of employee shifts worked within a specified time frame, including required, preferred, and conditional constraints.
- [Shifts worked per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/shifts-worked-per-period.html): Explains how to configure and enforce rules for the number of shifts employees can work per period, including required and preferred limits, in employee shift scheduling.
- [Weekend minutes worked per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/weekend-minutes-worked-per-period.html): Describes how to configure and enforce limits on the number of weekend minutes employees can work per period using required or preferred contract rules in shift scheduling.
- [Weekends worked in a rolling window](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/weekends-worked-in-a-rolling-window.html): Explains how to configure and enforce rules for limiting weekends worked by employees within a rolling window in shift scheduling, including required, preferred, and conditional constraints.
- [Weekends worked per period](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/weekends-worked-per-period.html): Explains how to configure and enforce rules that limit the number of weekends an employee can work per period in shift scheduling, including required and preferred constraints.
- [Work limits](https://docs.timefold.ai/employee-shift-scheduling/latest/employee-resource-constraints/work-limits/work-limits.html): Explains how to configure and manage employee work limits, including contractual hours, overtime, weekend restrictions, and handling unpaid breaks in shift scheduling.
- [Cost groups](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/cost-management/cost-groups.html): Explains how to configure and use cost groups in employee shift scheduling, including assigning costs, setting budget limits, and filtering by employee or shift tags.
- [Cost management](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/cost-management/cost-management.html): Explains strategies and tools for managing and optimizing costs within employee shift scheduling scenarios.
- [Employee rates](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/cost-management/employee-rates.html): Describes how to define and manage employee base and overtime pay rates, including cost definitions, activation costs, and handling mixed recurrent costs in shift scheduling.
- [Employee selection](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/shift-assignments/employee-selection.html): Explains how to configure preferred, unpreferred, and prohibited employees for shift assignments in employee shift scheduling, including their effects on assignment constraints and scoring.
- [Shift assignments](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/shift-assignments/shift-assignments.html): Explains how to manage employee shift assignments, including workload coverage, resource constraints, and employee preferences or restrictions for specific shifts.
- [Shift selection](https://docs.timefold.ai/employee-shift-scheduling/latest/shift-service-constraints/shift-assignments/shift-selection.html): Explains how to configure and apply rules for assigning employees to the correct number of shifts per period, including required and preferred constraints, in employee shift scheduling.
- [Input datasets](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/input-datasets/input-datasets.html): Explains how to define, structure, and use input datasets with Timefold's Employee Shift Scheduling model API for optimization tasks.
- [Model configuration](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/input-datasets/model-configuration.html): Explains the structure and usage of the model configuration object in Timefold's Employee Shift Scheduling input datasets, including run settings and constraint weight overrides.
- [Model input](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/input-datasets/model-input.html): Describes the structure and fields of the modelInput JSON object for supplying employee, shift, contract, and scheduling data to the Employee Shift Scheduling model API.
- [Planning window](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/input-datasets/planning-window.html): Explains how the planning window defines the scheduling interval for employee shifts, including default derivation rules and when to specify it explicitly in shift scheduling data.
- [Input metrics](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/output-datasets/input-metrics.html): Describes the structure and meaning of the inputMetrics object, detailing each metric calculated from the input dataset for Employee Shift Scheduling troubleshooting.
- [Key performance indicators (KPIs)](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/output-datasets/key-performance-indicators.html): Describes the structure and meaning of the KPIs included in output datasets for Timefold's Employee Shift Scheduling model API, detailing each available metric.
- [Metadata](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/output-datasets/metadata.html): Explains the structure and contents of metadata fields included in output datasets generated by Timefold's Employee Shift Scheduling model API.
- [Output datasets](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/output-datasets/output-datasets.html): Explains the structure and contents of output datasets generated by Timefold's Employee Shift Scheduling model API after solving an input scheduling problem.
- [Model output](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/output-datasets/model-output.html): Explains the structure and contents of the modelOutput object in output datasets, detailing shift assignments and employee metrics for the Employee Shift Scheduling model API.
- [API tooling](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/using-the-api/api-tooling.html): Use the Employee Shift Scheduling OpenAPI spec to generate API clients, import Postman collections, or create Bruno collections.
- [Using the API](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/using-the-api/using-the-api.html): Explore the Employee Shift Scheduling API reference and learn how to use API tooling.
- [Using the OpenAPI spec](https://docs.timefold.ai/employee-shift-scheduling/latest/user-guide/using-the-api/using-the-openapi-spec.html): Understand how to use and explore the Employee Shift Scheduling OpenAPI specification.
- [Changelog](https://docs.timefold.ai/field-service-routing/latest/changelog.html): The changelog documents all updates, new features, enhancements, and fixes introduced in Timefold's Field Service Routing model across each released version.
- [Feature requests](https://docs.timefold.ai/field-service-routing/latest/feature-requests.html): Explains how to submit, track, and manage feature requests for the Timefold platform, including guidelines for users and contributors.
- [Getting started: Hello world](https://docs.timefold.ai/field-service-routing/latest/getting-started-with-field-service-routing.html): Provides a step-by-step hello world example for using Timefold's Field Service Routing model, including dataset structure, required fields, API usage, and interpreting the solution output.
- [Introduction](https://docs.timefold.ai/field-service-routing/latest/introduction.html): The Introduction page provides an overview of Timefold's Field Service Routing model, its core constraints, integration options, APIs, and guidance for developers, architects, and decision makers.
- [Manual intervention](https://docs.timefold.ai/field-service-routing/latest/manual-intervention.html): Explains how and why to manually assign or pin visits in field service routing, including gradual adoption, real-time planning adjustments, and experimental scenarios.
- [Upgrade to the latest version](https://docs.timefold.ai/field-service-routing/latest/upgrade-to-the-latest-version.html): This page documents how to upgrade between versions of the Field Service Routing model, detailing API changes, new output metrics, and required client code adjustments for each release.
- [Real-time planning: actual arrival and departure times](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-actual-times.html): Learn how to use actual arrival, service start, and departure times when re-planning in field service routing.
- [Real-time planning: emergency visit](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-emergency-visit.html): Describes how to update a field service routing schedule in real time to accommodate an emergency visit, including input data changes and API usage.
- [Real-time planning: extended visit](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-extended-visit.html): Explains how to update field service routing plans in real time when a scheduled visit takes longer than expected, including input adjustments and API usage.
- [Real-time planning: no show](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-no-show.html): Explains how to update real-time field service routing plans when a scheduled customer is a no show, including input adjustments and rescheduling steps.
- [Real-time planning: pinning visits](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-pinning-visits.html): Explains how to use block pinning and individual visit pinning to control which visits remain fixed during real-time planning in field service routing scenarios.
- [Real-time planning: reassignment](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-reassignment.html): Explains how to handle reassignment of visits in real-time field service routing when a visit takes longer than expected, including updating input data and freezing completed visits.
- [Real-time planning: technician ill](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning-technician-ill.html): Describes how to update field service routing plans in real time when a technician becomes ill, including input adjustments and shift reassignment procedures.
- [Real-time planning](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/real-time-planning.html): This page explains how to adapt and update field service routing plans in real time, covering scenarios, process steps, and key data fields for continuous and emergency scheduling changes.
- [Bulk time window recommendations](https://docs.timefold.ai/field-service-routing/latest/recommendations/bulk-time-window-recommendations.html): Learn about bulk visit recommendations in field service routing.
- [Recommendations](https://docs.timefold.ai/field-service-routing/latest/recommendations/recommendations.html): Explains how Timefold generates and manages time window recommendations for customer service visits in field service routing scenarios.
- [Visit group time window recommendations](https://docs.timefold.ai/field-service-routing/latest/recommendations/visit-group-time-window-recommendations.html): Explains how to use the visit group time window recommendations API to suggest time windows for multi-vehicle visits in field service routing, including input structure, example requests, and pinning behavior.
- [Visit time window recommendations](https://docs.timefold.ai/field-service-routing/latest/recommendations/visit-time-window-recommendations.html): This page explains how to use the visit time window recommendations API to suggest optimal time windows for customer visits in field service routing, including input structure, example requests, and handling pinned visits.
- [Configuring labor law compliance](https://docs.timefold.ai/field-service-routing/latest/scenarios/configuring-labor-law-compliance.html): Explains how to configure Timefold’s Field Service Routing model to express and manage common labor law requirements for driving time, breaks, overtime, and rest periods.
- [Ferry Connections](https://docs.timefold.ai/field-service-routing/latest/scenarios/ferry-connections.html): Learn about ferry connections in field service routing.
- [Long-running visits](https://docs.timefold.ai/field-service-routing/latest/scenarios/long-running-visits.html): Explains how to handle long-running tasks in field service routing by splitting them into smaller visits, configuring model input, and considering alternatives like overtime.
- [Scenarios](https://docs.timefold.ai/field-service-routing/latest/scenarios/scenarios.html): Describes various scenarios encountered in field service routing, outlining different use cases and operational contexts relevant to scenario-based planning.
- [Constraints](https://docs.timefold.ai/field-service-routing/latest/user-guide/constraints.html): Explains the hard, medium, and soft constraints used in Timefold's Field Service routing model, including examples, scoring, and how to configure constraint weights.
- [Demo datasets](https://docs.timefold.ai/field-service-routing/latest/user-guide/demo-datasets.html): Explains how to access, download, and use field service routing demo datasets in Timefold Platform through both the UI and API, including example API calls.
- [Input validation](https://docs.timefold.ai/field-service-routing/latest/user-guide/input-validation.html): Explains how input dataset validation works in field service routing, detailing validation errors, warnings, their causes, and example issue details for visits and vehicle shifts.
- [Integration](https://docs.timefold.ai/field-service-routing/latest/user-guide/integration.html): Explains how to integrate Timefold with your existing systems, covering supported integration methods, configuration steps, and interoperability considerations.
- [Key performance indicators (KPIs)](https://docs.timefold.ai/field-service-routing/latest/user-guide/kpis.html): Describes the key performance indicators (KPIs) available in Timefold's Field Service Routing model, detailing each metric used to evaluate route plan quality.
- [Metrics and optimization goals](https://docs.timefold.ai/field-service-routing/latest/user-guide/metrics-and-optimization-goals.html): Explains the key metrics used in the Field Service Routing model and how these metrics relate to defining and achieving optimization goals.
- [Model response](https://docs.timefold.ai/field-service-routing/latest/user-guide/model-response.html): Describes the structure and contents of the model response for Timefold's Field Service Routing, including optimized route plans, dataset metadata, and key performance indicators.
- [Scheduling API concepts](https://docs.timefold.ai/field-service-routing/latest/user-guide/planning-ai-concepts.html): Introduces core concepts of Planning AI, including how optimization models are used to efficiently allocate resources and schedule tasks in real-world scenarios.
- [Routing with Timefold’s maps service](https://docs.timefold.ai/field-service-routing/latest/user-guide/routing-maps-service.html): Explains how to implement routing functionality in your model using Timefold’s maps service, including integration steps and supported features.
- [Score analysis](https://docs.timefold.ai/field-service-routing/latest/user-guide/score-analysis.html): Understand the score structure of your field service route plans.
- [Terminology](https://docs.timefold.ai/field-service-routing/latest/user-guide/terminology.html): Defines key terms used in field service routing, including constraints, datasets, planning windows, route plans, skills, vehicles, visits, and related scheduling concepts.
- [Use case guide](https://docs.timefold.ai/field-service-routing/latest/user-guide/use-cases.html): This page explains how to apply the Field Service Routing model to real-world use cases like technician dispatch, home care scheduling, and facility maintenance, detailing challenges and recommended configuration strategies for each.
- [User guide](https://docs.timefold.ai/field-service-routing/latest/user-guide/user-guide.html): The user guide introduces and explains the main concepts, features, and usage patterns of Timefold's Field Service Routing model.
- [Visualizations](https://docs.timefold.ai/field-service-routing/latest/user-guide/visualizations.html): This page explains how to use the Field Service Routing model’s Map, By Vehicle, and By Visit visualizations to validate schedules, analyze routes, and identify operational issues.
- [Coverage area](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/coverage-area.html): Learn about coverage areas in field service routing.
- [Fairness](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/fairness.html): Explains how to achieve balanced workloads among technicians in field service routing using fairness constraints, including examples with and without historical data and guidance on relevant input fields.
- [Lunch breaks and personal appointments](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/lunch-breaks-and-personal-appointments.html): Describes how to schedule lunch breaks, personal appointments, and team meetings for technicians within field service routing, including floating and fixed break types, input data structure, and scheduling constraints.
- [Route optimization](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/route-optimization.html): This page explains how to configure and use Timefold's route optimization features, including minimizing travel time, distance, and waiting, and handling unreachable routes for technician scheduling.
- [Shift hours and overtime](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/shift-hours-and-overtime.html): Explains how to configure vehicle shift hours, overtime rules, and related scheduling constraints for technicians in field service routing, including JSON examples and constraint behavior.
- [Technician costs](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/technician-costs.html): Explains how to model and optimize technician costs in field service routing, covering employee and contractor wage structures, shift rates, fixed and activation costs, and cost-based assignment examples.
- [Technician ratings](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/technician-ratings.html): Explains how to define, configure, and use technician ratings in Timefold's Field Service Routing model to prioritize higher-rated technicians during route planning.
- [Vehicle resource constraints](https://docs.timefold.ai/field-service-routing/latest/vehicle-resource-constraints/vehicle-resource-constraints.html): Explains the concept of vehicle resource constraints in field service routing, focusing on managing technicians and their vehicles within optimization scenarios.
- [Duration added for first visit on location](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/duration-added-for-first-visit-on-location.html): Explains how to configure and use the durationAddedForFirstVisitOnLocation setting to model extra overhead time for technicians' first arrival at a location during a shift.
- [Multi-day schedules and movable visits](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/multi-day-schedules-and-movable-visits.html): Explains how to configure and manage multi-day schedules and movable visits in field service routing, including time windows, scheduling strategies, and the movableOccupationRatioThreshold setting.
- [Multi-vehicle visits](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/multi-vehicle-visits.html): Explains how to schedule and configure multi-vehicle visits in field service routing, including visit groups, technician skills, service duration strategies, and relevant API examples.
- [Priority visits and optional visits](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/priority-visits-and-optional-visits.html): Explains how to configure and prioritize mandatory and optional visits in field service routing, including assigning priorities, handling planning windows, and managing unassigned visits.
- [Skills](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/skills.html): Explains how to define technician skills, skill levels, and temporary skills for field service routing, including how to match required skills to visits and configure skill-based assignment constraints.
- [Visit dependencies](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-dependencies.html): Explains how to define and schedule dependent visits in field service routing, including single and multiple dependencies, required order, delays, and mutually exclusive visits.
- [Visit profit](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-profit.html): Explains how to use the visit profit feature in Timefold to prioritize and schedule the most profitable visits when technician capacity is limited, including configuration and example usage.
- [Visit service constraints](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-service-constraints.html): Explains the various aspects of visit service constraints in field service routing, focusing on how visits are managed and scheduled within optimization solutions.
- [Visit service level agreement (SLA)](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-slas.html): Explains how to define and use visit service level agreements (SLAs) in field service routing, including the `latestSlaEndTime` property, scoring penalties, and example API usage.
- [Time windows and opening hours](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-time-windows.html): Explains how to define and use required and preferred time windows and opening hours for scheduling field service visits, including examples and API usage details.
- [Real-time planning: emergency visit (using patches)](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning-emergency-visit.html): Describes how to use patch operations to add emergency visits and update schedules in real-time field service routing, including API examples and input/output formats.
- [Real-time planning: extended visit (using patches)](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning-extended-visit.html): Describes how to use patch operations in real-time planning to handle field service visits that take longer than expected, including updating schedules and freezing completed tasks.
- [Real-time planning: no show (using patches)](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning-no-show.html): Explains how to update a field service routing plan in real time using patch operations when a scheduled customer visit results in a no show.
- [Real-time planning: pinning visits (using patches)](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning-pinning-visits.html): Explains how to use patch operations to pin visits in real-time field service routing, including block pinning, individual visit pinning, and required input formats.
- [Real-time planning: reassignment (using patches)](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning-reassignment.html): This page explains how to use patch operations to update and reassign visits in real-time field service routing when unexpected delays occur.
- [Real-time planning: technician ill (using patches)](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning-technician-ill.html): Explains how to update field service routing plans in real time using patch operations when a technician becomes ill before or during a shift.
- [Real-time planning with patches](https://docs.timefold.ai/field-service-routing/latest/real-time-planning/preview/real-time-planning.html): This page explains how to use patch functionality to adapt and update field service routing plans in real time as circumstances change during execution.
- [Input datasets](https://docs.timefold.ai/field-service-routing/latest/user-guide/input-datasets/input-datasets.html): Explains what input datasets are and how they are used to define and optimize problems with Timefold's Field Service Routing model API.
- [Model configuration](https://docs.timefold.ai/field-service-routing/latest/user-guide/input-datasets/model-configuration.html): Explains the structure and options of the model configuration object in Timefold's Field Service Routing input dataset, including constraint overrides, travel time adjustments, and priority settings.
- [Model input](https://docs.timefold.ai/field-service-routing/latest/user-guide/input-datasets/model-input.html): Explains the structure and fields of the modelInput JSON used to define technicians, visits, skills, tags, and scheduling parameters for Timefold's Field Service Routing model API.
- [Planning window](https://docs.timefold.ai/field-service-routing/latest/user-guide/input-datasets/planning-window.html): Explains the concept of a planning window in field service routing, including its purpose, automatic derivation, and how to define start and end dates.
- [Time zones and daylight-saving time (DST)](https://docs.timefold.ai/field-service-routing/latest/user-guide/input-datasets/time-zones-and-dst.html): Explains how to handle multiple time zones and daylight-saving time in field service routing by using ISO 8601 date-times with UTC offsets or converting all times to UTC.
- [Input metrics](https://docs.timefold.ai/field-service-routing/latest/user-guide/output-datasets/input-metrics.html): Describes the structure and purpose of the input metrics collected in output datasets for Timefold's Field Service Routing model API, including example metric fields.
- [Key performance indicators (KPIs)](https://docs.timefold.ai/field-service-routing/latest/user-guide/output-datasets/key-performance-indicators.html): Describes the structure and meaning of the KPIs object in Timefold's Field Service Routing output datasets, detailing available performance metrics and their use in solution evaluation.
- [Metadata](https://docs.timefold.ai/field-service-routing/latest/user-guide/output-datasets/metadata.html): Describes the metadata fields included in output datasets generated by Timefold's Field Service Routing model API.
- [Model output](https://docs.timefold.ai/field-service-routing/latest/user-guide/output-datasets/model-output.html): Describes the structure and contents of the modelOutput object in Timefold's Field Service Routing output datasets, including vehicles, shifts, assigned and unassigned visits, and associated metrics.
- [Output datasets](https://docs.timefold.ai/field-service-routing/latest/user-guide/output-datasets/output-datasets.html): Explains the structure and contents of output datasets generated by Timefold's Field Service Routing model API after solving an input scheduling problem.
- [API tooling](https://docs.timefold.ai/field-service-routing/latest/user-guide/using-the-api/api-tooling.html): Use the Field Service Routing OpenAPI spec to generate API clients, import Postman collections, or create Bruno collections.
- [Using the API](https://docs.timefold.ai/field-service-routing/latest/user-guide/using-the-api/using-the-api.html): Explore the Field Service Routing API reference and learn how to use API tooling.
- [Using the OpenAPI spec](https://docs.timefold.ai/field-service-routing/latest/user-guide/using-the-api/using-the-openapi-spec.html): Understand how to use and explore the Field Service Routing OpenAPI specification.
- [Tags](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-requirements-and-tags/tags.html): Explains how to define, assign, and validate tags and temporary tags for technicians and visits in field service routing models to enforce assignment requirements.
- [Visit requirements and tags](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-requirements-and-tags/visit-requirement-and-tags.html): Explains how to model customer-specific visit requirements and use tags to represent assignment restrictions in field service routing scenarios.
- [Visit requirements](https://docs.timefold.ai/field-service-routing/latest/visit-service-constraints/visit-requirements-and-tags/visit-requirements.html): This page explains how to specify required, preferred, and prohibited technicians for customer visits in field service routing, including relevant JSON attributes and constraint behavior.
- [Changelog](https://docs.timefold.ai/pickup-delivery-routing/latest/changelog.html): The changelog documents all updates, new features, API changes, and improvements made to Timefold's Pick-up and Delivery Routing model across each released version.
- [Feature requests](https://docs.timefold.ai/pickup-delivery-routing/latest/feature-requests.html): Explains how to submit, track, and manage feature requests for the Timefold platform, including guidelines for users and contributors.
- [Getting started: Hello world](https://docs.timefold.ai/pickup-delivery-routing/latest/getting-started-with-pickup-delivery-routing.html): This page walks through a complete hello world example for Timefold's Pick-up and Delivery Routing model, including dataset structure, required fields, API usage, and interpreting the solution output.
- [Introduction](https://docs.timefold.ai/pickup-delivery-routing/latest/introduction.html): The Introduction page explains the core concepts, features, constraints, APIs, and integration options of Timefold's Pick-up and Delivery Routing model for planners, developers, and decision makers.
- [Upgrading to the latest versions](https://docs.timefold.ai/pickup-delivery-routing/latest/upgrade-to-the-latest-version.html): Describes how to upgrade between versions of the Pick-up and Delivery Routing model, detailing API changes, renamed fields, and required input and output adjustments.
- [Driver capacity](https://docs.timefold.ai/pickup-delivery-routing/latest/driver-resource-constraints/driver-capacity.html): Explains how to define and enforce driver vehicle capacity constraints in pick-up and delivery routing, including JSON configuration for capacities and demands, and validation rules.
- [Driver resource constraints](https://docs.timefold.ai/pickup-delivery-routing/latest/driver-resource-constraints/driver-resource-constraints.html): Explains the various aspects of managing driver resource constraints in pick-up and delivery routing scenarios.
- [Lunch breaks and personal appointments](https://docs.timefold.ai/pickup-delivery-routing/latest/driver-resource-constraints/lunch-breaks-and-personal-appointments.html): Explains how to schedule floating and fixed lunch breaks and personal appointments for drivers in pick-up and delivery routing, including configuration options and constraints.
- [Route optimization](https://docs.timefold.ai/pickup-delivery-routing/latest/driver-resource-constraints/route-optimization.html): Explains how Timefold optimizes driver routes to minimize travel time and penalizes unreachable itineraries or stops in pick-up and delivery scenarios.
- [Shift hours and overtime](https://docs.timefold.ai/pickup-delivery-routing/latest/driver-resource-constraints/shift-hours-and-overtime.html): Explains how to configure driver shift hours, overtime, start and end locations, and related constraints for pick-up and delivery routing scenarios.
- [Dependencies between stops](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/dependencies-between-stops.html): Explains how to define and enforce dependencies between stops in pick-up and delivery routing, including required stop order, driver assignment, and related hard constraints.
- [Job service constraints](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-service-constraints.html): Explains the various job service constraints involved in managing jobs within pick-up and delivery routing scenarios.
- [Multi-day schedules and movable stops](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/multi-day-schedules-and-movable-stops.html): Explains how to handle movable stops and multi-day schedules in pick-up and delivery routing, including scheduling logic and the earliest-day soft constraint with practical examples.
- [Priority jobs and optional jobs](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/priority-jobs-and-optional-jobs.html): Explains how to configure and use priority levels, mandatory jobs, and optional jobs in pick-up and delivery routing, including penalties and assignment behavior.
- [Skills](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/skills.html): Explains how to define driver skills and skill levels, specify required skills for jobs, and how these constraints affect driver-job assignments in pick-up and delivery routing.
- [Stop service level agreement (SLA)](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/stop-slas.html): Explains how to define and optimize for stop service level agreements (SLAs) in pick-up and delivery routing using the `latestSlaEndTime` property and associated soft constraints.
- [Time windows and opening hours](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/time-windows-and-opening-hours.html): Explains how to define and use time windows and opening hours for pick-up and delivery routing jobs, including API examples and constraint behavior for scheduling stops.
- [Real-time planning: driver ill](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/real-time-planning-driver-ill.html): Learn how to create real-time plans in pick-up and delivery routing when a driver is ill.
- [Real-time planning: extended stop](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/real-time-planning-extended-stop.html): Learn how to manage stops that take longer than expected with real-time planning in pick-up and delivery routing.
- [Real-time planning: no show](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/real-time-planning-no-show.html): Learn how to create real-time plans in pick-up and delivery routing when a customer is a no show.
- [Real-time planning: pinning stops](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/real-time-planning-pinning-stops.html): Explains how to use pinning features in real-time pick-up and delivery routing to prevent specific stops or driver shifts from being rescheduled, including examples with freezeTime and individual stop pinning.
- [Real-time planning: reassignment](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/real-time-planning-reassignment.html): Learn how to reassign stops with real-time planning in pick-up and delivery routing.
- [Real-time planning](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/real-time-planning.html): Explains how to use real-time planning in pick-up and delivery routing to update existing route plans dynamically when jobs, drivers, or constraints change.
- [Job time window recommendations](https://docs.timefold.ai/pickup-delivery-routing/latest/recommendations/job-time-window-recommendations.html): Explains how to use the job time window recommendations API to suggest feasible time windows for scheduling pick-up and delivery jobs based on driver shifts and input constraints.
- [Recommendations](https://docs.timefold.ai/pickup-delivery-routing/latest/recommendations/recommendations.html): Explains how Timefold generates and manages time window recommendations for pick-up and delivery jobs, allowing customers to select preferred service times before optimization.
- [Stop time window recommendations](https://docs.timefold.ai/pickup-delivery-routing/latest/recommendations/stops-time-window-recommendations.html): Explains how to request and interpret stop-level time window recommendations for pick-up and delivery routing, including API usage, input structure, and example scenarios.
- [Constraints](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/constraints.html): Explains the hard, medium, and soft constraints in Timefold's Pick-up and Delivery model, including examples, constraint groups, and how constraint weights affect optimization scoring.
- [Demo datasets](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/demo-datasets.html): Explains how to access, download, and use pick-up and delivery routing demo datasets in Timefold Platform through both the UI and API.
- [Input validation](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/input-validation.html): Explains the rules, errors, warnings, and solutions for validating input datasets in pick-up and delivery routing, including driver shifts, jobs, stops, and time windows.
- [Integration](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/integration.html): Explains how to integrate Timefold with your existing systems, covering supported integration methods, configuration steps, and best practices for connecting external applications.
- [Metrics and optimization goals](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/metrics-and-optimization-goals.html): Explains the role of metrics and optimization goals in the Pick-up and Delivery Routing model, including where to find metric definitions and how goals are aligned and configured.
- [Scheduling API concepts](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/planning-ai-concepts.html): Introduces core concepts of Planning AI, focusing on how to schedule pick-up and delivery jobs efficiently by managing drivers, locations, and timing.
- [Routing with Timefold’s maps service](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/routing-maps-service.html): Explains how to implement and use routing functionality in your model by integrating with Timefold’s maps service.
- [Score analysis](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/score-analysis.html): Understand the score structure of your pick-up and delivery route plans.
- [Terminology](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/terminology.html): Defines key terms and concepts used in pick-up and delivery routing, including capacity, constraints, datasets, demand, driver, job, load, planning window, shift, and stop.
- [Use case guide](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/use-cases.html): This page explains how to apply the Pick-up and Delivery Routing model to real-world use cases like non-emergency medical transportation and courier delivery, detailing challenges and recommended model features for each scenario.
- [User guide](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/user-guide.html): The User guide introduces Timefold's Pick-up and Delivery Routing model and outlines the main topics covered for users working with this feature.
- [Job pooling](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-requirements-and-tags/job-pooling.html): Explains how job pooling works in Timefold's Pick-up and Delivery Routing model, including how to prohibit pooling for specific jobs using the prohibitJobPooling setting.
- [Job required drivers](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-requirements-and-tags/job-required-drivers.html): Explains how to specify required, preferred, and prohibited drivers for jobs in Timefold's Pick-up and Delivery Routing model, including JSON examples and constraint behavior.
- [Job requirements and tags](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-requirements-and-tags/job-requirements-and-tags.html): Describes how to specify and use job requirements and tags for different jobs in pick-up and delivery routing scenarios.
- [Maximum time burden](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-requirements-and-tags/maximum-time-burden.html): Describes how to configure and enforce a maximum allowed time between pick-up and delivery for jobs in the Pick-up and Delivery Routing model, using global or job-specific settings.
- [Prohibit job combinations](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-requirements-and-tags/prohibited-job-combinations.html): Explains how to prevent specific jobs from being assigned together in Timefold's Pick-up and Delivery Routing model using the prohibitedJobs constraint and API examples.
- [Tags](https://docs.timefold.ai/pickup-delivery-routing/latest/job-service-constraints/job-requirements-and-tags/tags.html): Explains how to use tags to represent driver certifications and job requirements in pick-up and delivery routing, including JSON configuration and assignment logic.
- [Real-time planning: driver ill (using patches)](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/preview/real-time-planning-driver-ill.html): Learn how to use the patch functionality to create real-time plans in pick-up and delivery routing when a driver is ill.
- [Real-time planning: extended stop (using patches)](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/preview/real-time-planning-extended-stop.html): Learn how to use the patch functionality to manage stops that take longer than expected with real-time planning in pick-up and delivery routing.
- [Real-time planning: no show (using patches)](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/preview/real-time-planning-no-show.html): Learn how to use the patch functionality to create real-time plans in pick-up and delivery routing when a customer is a no show.
- [Real-time planning: pinning stops (using patches)](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/preview/real-time-planning-pinning-stops.html): Learn how to use the patch functionality when pinning stops for real-time planning in pick-up and delivery routing.
- [Real-time planning: reassignment (using patches)](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/preview/real-time-planning-reassignment.html): Learn how to use the patch functionality to reassign stops with real-time planning in pick-up and delivery routing.
- [Real-time planning with patches](https://docs.timefold.ai/pickup-delivery-routing/latest/real-time-planning/preview/real-time-planning.html): Learn about using the patch functionality with real-time planning in pick-up and delivery routing.
- [Input datasets](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/input-datasets/input-datasets.html): Explains how to define, structure, and use input datasets for Timefold's Pick-up and Delivery Routing model API.
- [Model configuration](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/input-datasets/model-configuration.html): Explains how to configure the model section of an input dataset in Timefold's Pick-up and Delivery Routing model, including constraint weight overrides and the maximumTimeBurden parameter.
- [Model input](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/input-datasets/model-input.html): Explains the structure and fields of the modelInput JSON object for specifying drivers, jobs, planning windows, and priority weights in Pick-up and Delivery Routing datasets.
- [Planning window](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/input-datasets/planning-window.html): Explains the concept of a planning window in Timefold's Pick-up and Delivery Routing model, including its purpose, automatic derivation, and JSON definition format.
- [Input metrics](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/output-datasets/input-metrics.html): Describes the structure and purpose of the input metrics collected in output datasets for the Pick-up and Delivery Routing model API, including example fields.
- [Key performance indicators (KPIs)](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/output-datasets/key-performance-indicators.html): Describes the structure and meaning of the KPIs object in output datasets for Timefold's Pick-up and Delivery Routing model, detailing available performance metrics.
- [Metadata](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/output-datasets/metadata.html): Describes the metadata fields included in output datasets generated by Timefold's Pick-up and Delivery Routing model API.
- [Model output](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/output-datasets/model-output.html): Describes the structure and contents of the modelOutput object in output datasets for Timefold's Pick-up and Delivery Routing model API, including drivers, shifts, itineraries, metrics, and unassigned jobs.
- [Output datasets](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/output-datasets/output-datasets.html): Explains the structure and contents of output datasets generated by Timefold's Pick-up and Delivery Routing model API after solving a scheduling problem.
- [API tooling](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/using-the-api/api-tooling.html): Use the Pick-up and Delivery Routing OpenAPI spec to generate API clients, import Postman collections, or create Bruno collections.
- [Using the API](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/using-the-api/using-the-api.html): Explore the Pick-up and Delivery Routing API reference and learn how to use API tooling.
- [Using the OpenAPI spec](https://docs.timefold.ai/pickup-delivery-routing/latest/user-guide/using-the-api/using-the-openapi-spec.html): Understand how to use and explore the Pick-up and Delivery Routing OpenAPI specification.
- [Changelog](https://docs.timefold.ai/job-scheduling/latest/changelog.html): Documents the initial release and version history of Timefold's experimental Job Scheduling model, including references to OpenAPI specifications and getting started guides.
- [Feature requests](https://docs.timefold.ai/job-scheduling/latest/feature-requests.html): Explains how to submit, track, and manage feature requests for the Timefold platform, including guidelines for users and the review process.
- [Getting started: Hello world](https://docs.timefold.ai/job-scheduling/latest/getting-started-with-job-scheduling.html): This page walks through a complete "hello world" example for Timefold's Job Scheduling model, showing how to create, submit, and retrieve a simple job scheduling dataset and solution.
- [Introduction](https://docs.timefold.ai/job-scheduling/latest/introduction.html): Introduces Timefold's Job Scheduling model, explaining its core features, constraints, and REST API for managing optimization problems on the Timefold Platform.
- [Real-time planning](https://docs.timefold.ai/job-scheduling/latest/real-time-planning.html): Explains how to perform real-time planning and job pinning in the Job Scheduling model, including how to maintain or update assignments when schedules change.
- [Upgrading to the latest versions](https://docs.timefold.ai/job-scheduling/latest/upgrade-to-the-latest-version.html): Explains that upgrade instructions for moving between versions of the Job Scheduling model are not yet available.
- [Job dependencies](https://docs.timefold.ai/job-scheduling/latest/job-service-constraints/job-dependencies.html): Learn about job dependencies in Timefold's Task Scheduling model.
- [Job service constraints](https://docs.timefold.ai/job-scheduling/latest/job-service-constraints/job-service-constraints.html): Learn about job service constraints in task scheduling.
- [Priority jobs](https://docs.timefold.ai/job-scheduling/latest/job-service-constraints/priority-jobs.html): Learn about priority in Timefold's Task Scheduling model.
- [Tags and specific resources](https://docs.timefold.ai/job-scheduling/latest/job-service-constraints/tags-and-specific-resources.html): Learn about tags and specific resources in the Task Scheduling model.
- [Time management](https://docs.timefold.ai/job-scheduling/latest/job-service-constraints/time-management.html): Learn about time management in Timefold's Task Scheduling model.
- [Time windows](https://docs.timefold.ai/job-scheduling/latest/job-service-constraints/time-windows.html): Learn about time windows in task scheduling.
- [Employee resources](https://docs.timefold.ai/job-scheduling/latest/machine-resource-constraints/employee-resources.html): Learn about scheduling employee tasks with the Task Scheduling model.
- [Machine and employee resource constraints](https://docs.timefold.ai/job-scheduling/latest/machine-resource-constraints/machine-resource-constraints.html): Explains how machine resource constraints affect the management of machines and employees in job scheduling scenarios.
- [Machine unavailability](https://docs.timefold.ai/job-scheduling/latest/machine-resource-constraints/machine-unavailability.html): Explains how to model and handle periods when a production machine is unavailable, such as for maintenance or holidays, in the Job Scheduling model.
- [Resource transitions](https://docs.timefold.ai/job-scheduling/latest/machine-resource-constraints/resource-transitions.html): Learn about job transitions and resource transitions in the Task Scheduling model.
- [Constraints](https://docs.timefold.ai/job-scheduling/latest/user-guide/constraints.html): Describes the hard, medium, and soft constraints in Timefold's Job Scheduling model, detailing constraint groups, examples, and how constraint weights affect scheduling outcomes.
- [Demo datasets](https://docs.timefold.ai/job-scheduling/latest/user-guide/demo-datasets.html): Explains how to access, download, and use job scheduling demo datasets in Timefold Platform through both the user interface and API endpoints.
- [Freeze jobs until](https://docs.timefold.ai/job-scheduling/latest/user-guide/freeze-jobs-until.html): Explains how the freezeJobsUntil feature automatically pins jobs on machines or employees that start before a specified time, supporting real-time and rolling horizon planning scenarios.
- [Integration](https://docs.timefold.ai/job-scheduling/latest/user-guide/integration.html): Learn about Timefold integrating with your current systems.
- [Job types and machine types](https://docs.timefold.ai/job-scheduling/latest/user-guide/job-types-and-machine-types.html): Explains how to categorize jobs and machines using job types and machine types in the Job Scheduling model, including their role in modeling setup transitions and validation requirements.
- [Metrics and optimization goals](https://docs.timefold.ai/job-scheduling/latest/user-guide/metrics-and-optimization-goals.html): Explains the key metrics used in the Job Scheduling model and how they relate to defining and achieving optimization goals within Timefold.
- [Scheduling API concepts](https://docs.timefold.ai/job-scheduling/latest/user-guide/planning-ai-concepts.html): Explains core concepts of Planning AI, including how job scheduling models allocate resources like machines and employees for efficient task organization.
- [Resource-specific durations](https://docs.timefold.ai/job-scheduling/latest/user-guide/resource-specific-durations.html): Explains how to configure resource-specific job durations in the Job Scheduling model, allowing each job to take different amounts of time depending on the assigned machine or employee.
- [Score analysis](https://docs.timefold.ai/job-scheduling/latest/user-guide/score-analysis.html): Understand the score structure of your task schedules.
- [Terminology](https://docs.timefold.ai/job-scheduling/latest/user-guide/terminology.html): Learn the terminology used in task scheduling.
- [User guide](https://docs.timefold.ai/job-scheduling/latest/user-guide/user-guide.html): The user guide introduces Timefold's Job Scheduling model and outlines key concepts and topics relevant to using and understanding the system.
- [Validation](https://docs.timefold.ai/job-scheduling/latest/user-guide/validation.html): Explains how input dataset validation works in job scheduling, detailing common validation errors, warnings, and their solutions for resources, jobs, and tags.
- [Input datasets](https://docs.timefold.ai/job-scheduling/latest/user-guide/input-datasets/input-datasets.html): Learn about the input datasets used with Timefold's Task Scheduling model API
- [Model configuration](https://docs.timefold.ai/job-scheduling/latest/user-guide/input-datasets/model-configuration.html): Learn about the model configuration in Timefold's Task Scheduling model
- [Model input](https://docs.timefold.ai/job-scheduling/latest/user-guide/input-datasets/model-input.html): Learn about the model input in datasets used with Timefold's Task Scheduling model API
- [Input metrics](https://docs.timefold.ai/job-scheduling/latest/user-guide/output-datasets/input-metrics.html): Learn about the input metrics collected in the output datasets used with Timefold's Task Scheduling model API
- [Key performance indicators (KPIs)](https://docs.timefold.ai/job-scheduling/latest/user-guide/output-datasets/key-performance-indicators.html): Learn about the KPIs included in output datasets used with Timefold's Task Scheduling model API
- [Metadata](https://docs.timefold.ai/job-scheduling/latest/user-guide/output-datasets/metadata.html): Learn about the metadata included in output datasets used with Timefold's Task Scheduling model API
- [Model output](https://docs.timefold.ai/job-scheduling/latest/user-guide/output-datasets/model-output.html): Learn about the model output included in output datasets used with Timefold's Task Scheduling model API
- [Output datasets](https://docs.timefold.ai/job-scheduling/latest/user-guide/output-datasets/output-datasets.html): Learn about the output datasets used with Timefold's Task Scheduling model API
- [API tooling](https://docs.timefold.ai/job-scheduling/latest/user-guide/using-the-api/api-tooling.html): Use the Task Scheduling OpenAPI spec to generate API clients, import Postman collections, or create Bruno collections.
- [Using the API](https://docs.timefold.ai/job-scheduling/latest/user-guide/using-the-api/using-the-api.html): Explore the Task Scheduling API reference and learn how to use API tooling.
- [Using the OpenAPI spec](https://docs.timefold.ai/job-scheduling/latest/user-guide/using-the-api/using-the-openapi-spec.html): Understand how to use and explore the Task Scheduling OpenAPI specification.