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Field Service Routing

    • Introduction
    • Getting started with field service routing
    • User guide
      • User guide
      • Terms
      • Planning AI concepts
      • Constraints
      • Understanding the API
      • Planning window
      • Model configuration
      • Configuration overrides
      • Time zones and daylight-saving time (DST)
      • Routing with Timefold’s maps service
      • Validation
      • Model response
      • Key performance indicators (KPIs)
      • Metrics and optimization goals
    • Vehicle resource constraints
      • Vehicle resource constraints
      • Shift hours and overtime
      • Lunch breaks and personal appointments
      • Fairness
      • Route optimization
      • Technician coverage area
      • Technician costs
      • Technician ratings
    • Visit service constraints
      • Visit service constraints
      • Time windows and opening hours
      • Skills
      • Visit dependencies
      • Visit requirements
      • Multi-vehicle visits
      • Movable visits and multi-day schedules
      • Priority visits and optional visits
      • Visit service level agreement (SLA)
    • Recommendations
      • Recommendations
      • Visit time window recommendations
      • Visit group time window recommendations
    • Real-time planning
      • Real-time planning
      • Real-time planning: extended visit
      • Real-time planning: reassignment
      • Real-time planning: emergency visit
      • Real-time planning: no show
      • Real-time planning: technician ill
      • Real-time planning: pinning visits
    • Real-time planning (preview)
      • Real-time planning (preview)
      • Real-time planning: extended visit (preview)
      • Real-time planning: reassignment (preview)
      • Real-time planning: emergency visit (preview)
      • Real-time planning: no show (Preview)
      • Real-time planning: technician ill (Preview)
      • Real-time planning: pinning visits (preview)
    • Scenarios
      • Long-running visits
    • 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 service customer visits in an efficient and effective manner you need enough qualified field service technicians working in the appropriate areas.

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 field service routing.

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 field service routing from our YouTube playlist.

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