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  • Manual intervention

Field Service Routing

    • Introduction
    • Getting started: Hello world
    • User guide
      • Terminology
      • Use case guide
      • Planning AI concepts
      • Integration
      • Constraints
      • Understanding the API
      • Demo datasets
      • Input datasets
        • Model configuration
        • Model input
        • Planning window
        • Time zones and daylight-saving time (DST)
      • Routing with Timefold’s maps service
      • Input validation
      • Model response
      • Output datasets
        • Metadata
        • Model output
        • Input metrics
        • Key performance indicators (KPIs)
      • Key performance indicators (KPIs)
      • Metrics and optimization goals
      • Visualizations
    • Vehicle resource constraints
      • Shift hours and overtime
      • Lunch breaks and personal appointments
      • Fairness
      • Route optimization
      • Technician costs
      • Technician ratings
      • Technician coverage area
    • Visit service constraints
      • Time windows and opening hours
      • Skills
      • Visit dependencies
      • Multi-vehicle visits
      • Multi-day schedules and movable visits
      • Priority visits and optional visits
      • Visit service level agreement (SLA)
      • Duration added for first visit on location
      • Visit profit
      • Visit requirements and tags
        • Visit requirements
        • Tags
    • Manual intervention
    • Recommendations
      • Visit time window recommendations
      • Visit group time window recommendations
    • 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 with patches
      • Real-time planning: extended visit (using patches)
      • Real-time planning: reassignment (using patches)
      • Real-time planning: emergency visit (using patches)
      • Real-time planning: no show (using patches)
      • Real-time planning: technician ill (using patches)
      • Real-time planning: pinning visits (using patches)
    • Scenarios
      • Long-running visits
      • Configuring labor law compliance
    • Changelog
    • Upgrade to the latest version
    • Feature requests

Manual intervention

Different industries have different scheduling requirements, and it is not always possible to automatically assign and optimize every route.

In field service routing you can pin one or many visits when a specific technician must be assigned to a specific visit or sequence of visits.

Even after you’ve implemented fully automated and optimized routing, there are still times when it is necessary to manually intervene and assign specific visits manually. For more information about pinning see Pinning visits in real-time planning.

This guide explains the benefits of using pinning in different scenarios:

  • Gradual adoption of the technology
  • Real-time planning
  • Experiments

Gradual adoption of the technology

Complex technology is rarely adopted all at once.

With pinning you can gradually introduce Timefold into the routing process as you build trust in the plans it generates. This also gives you time to fine-tune the constraints and constraint weights used in your datasets.

To gradually adopt Timefold, decide which portion of the route plan Timefold optimizes (for instance, starting with one region, depot, or team of technicians) and gradually increase the scope over time.

Real-time planning

Real-time planning makes it possible to update a route plan while it’s being executed, for instance, to add an emergency visit, handle a technician calling in sick, or account for a job that is running over. You can use pinning in real-time planning to minimize the disruption caused by replanning.

See the Pinning visits in real-time planning guide for more information.

Experiments

Pinning can also be used to run experiments on your datasets. By pinning a portion of the dataset you can answer "what would the result be if" questions that help you improve the quality of your plans over time. For instance, you might pin one technician’s route and observe how the optimizer assigns the remaining visits, or pin a visit with a specific time constraint to evaluate how the rest of the schedule adapts.

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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