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  • Employee Shift Scheduling
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  • Planning AI concepts

Employee Shift Scheduling

    • Introduction
    • Getting started with employee shift scheduling
    • User guide
      • Terms
      • Planning AI concepts
      • Constraints
      • Understanding the API
      • Planning window
      • Time zones and Daylight Saving Time (DST)
      • Tags and tag types
      • Validation
      • Metrics and optimization goals
      • Score analysis
    • Employee resource constraints
      • Employee availability
      • Employee contracts
      • Pairing employees
      • Shift travel and locations
      • Work limits
        • Minutes worked per period
        • Minutes worked in a rolling window
        • Minutes logged per period
        • Days worked per period
        • Days worked in a rolling window
        • Consecutive days worked
        • Shifts worked per period
        • Shifts worked in a rolling window
        • Weekend minutes worked per period
        • Weekends worked per period
        • Weekends worked in a rolling window
        • Consecutive weekends worked
      • Time off
        • Days off per period
        • Consecutive days off per period
        • Consecutive days off in a rolling window
        • Consecutive minutes off in a rolling window
        • Shifts to avoid close to day off requests
      • Shift rotations and patterns
        • Shift rotations
        • Single day shift sequence patterns
        • Minimize gaps between shifts
        • Multi-day shift sequence patterns
        • Daily shift pairings
        • Overlapping shifts
        • Shift start times differences
        • Minutes between shifts
      • Shift type diversity
        • Shift types worked per period
        • Unique tags per period
      • Fairness
        • Balance time worked
        • Balance shift count
    • Shift service constraints
      • Alternative shifts
      • Cost management
      • Demand-based scheduling
      • Mandatory and optional shifts
      • Shift assignments
      • Skills and risk factors
    • Recommendations
    • Real-time planning
    • Real-time planning (preview)
    • Changelog
    • Upgrade to the latest version
    • Feature requests

Planning AI concepts

Planning

The need to create plans generally arises from a desire to achieve a goal:

  • Build a house.

  • Correctly staff a hospital shift.

  • Complete work at all customer locations.

Achieving those goals involves organizing the available resources. To correctly staff a hospital you need enough qualified personnel in a variety of fields and specializations to cover the opening hours of the hospital.

Constraints

Any plan to deploy resources, is done under constraints.

Constraints could be laws of the universe; people can’t work two shifts in two separate locations at the same time, and you can’t mount a roof on a house that doesn’t exist. Constraints can also be relevant legislation; employees need a certain number of hours between shifts or are only allowed to work a maximum number of hours per week.

Learn more about constraints in employee shift scheduling.

Feasible plans

Any plan needs to consider all three elements, goals, resources, and constraints, in balance to be a feasible plan. A plan that fails to account for all the elements of the problem is an infeasible plan. For instance, if a hospital staff roster covers all shifts, but assigns employees back-to-back shifts with no breaks for sleep or life outside work, it is not a valid plan.

Planning problems are hard to solve

Planning problems become harder to solve as the number of resources and constraints increase. Creating a schedule for a small team of four employees is fairly straightforward. However, if each employee performs a specific function within the business and those functions need to be performed in a specific order, changes that affect one employee quickly cascade and affect everybody on the team. If parts are delivered late and prevent one employee from completing their tasks, subsequent work will also be delayed.

As more employees and different work specializations are added, things become much more complicated.

For a trivial field service routing problem with 4 vehicles and 8 visits, the number of possibilities that a brute algorithm considers is 19,958,418.

What would take a team of planners many hours to schedule can be automatically scheduled by Timefold in a fraction of the time.

Operations Research

Operations Research (OR) is a field of research that is focused on finding optimal (or near optimal) solutions to problems with techniques that improve decision-making.

Constraint satisfaction programming is part of Operations Research that aims to satisfy all the constraints of a problem.

Planning AI

Planning AI is a type of artificial intelligence designed specifically to handle complex planning and scheduling tasks and to satisfy the constraints of planning problems. Instead of just automating simple, repetitive tasks, it helps you make better decisions by sorting through countless possibilities to find the best solutions—saving you time, reducing costs, and improving efficiency.

Why Planning AI is different

Traditional methods of planning often involve manually sifting through options or relying on basic tools that can’t keep up with the complexity of real-world problems. Planning AI, on the other hand, uses advanced strategies to quickly focus on the most promising solutions, even when the situation is extremely complicated. Planning AI also makes it possible to understand the final solution with a breakdown of which constraints have been violated and scores for individual constraints and an overall score. This makes Planning AI incredibly valuable in industries where getting the right plan is crucial—whether that’s scheduling workers, routing deliveries, or managing resources in a factory.

Planning AI is designed to be accessible, so you can start improving your planning process right away.

Timefold Platform

Timefold Platform is Timefold’s managed SaaS that’s built on top of Timefold’s open source Solver technology.

The Platform provides easy access to Timefold’s models through the REST API to integrate our solver technology and models with your apps. Timefold Platform is a fully managed service, removing the need to manage infrastructure yourself. It comes with scalability, performance, and reliability benefits. The platform gives problem owners the right tools and insights to further optimize their planning solutions.

Timefold Platform can also be self-hosted. Please get in touch to discuss your requirements.

Learn more about Timefold Platform.

Next

  • See the full API spec or try the online API.

  • Learn more about employee shift scheduling from our YouTube playlist.

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