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  • Pick-up and Delivery Routing
  • Driver resource constraints
  • Route optimization

Pick-up and Delivery Routing

    • Introduction
    • Getting started: Hello world
    • User guide
      • Terms
      • Planning AI concepts
      • Constraints
      • Demo datasets
      • Validation
      • Routing with Timefold’s maps service
      • Metrics and optimization goals
    • Driver resource constraints
      • Lunch breaks and personal appointments
      • Route optimization
      • Shift hours and overtime
    • Job service constraints
      • Time windows and opening hours
      • Skills
      • Movable stops and multi-day schedules
      • Dependencies between stops
      • Priority jobs and optional jobs
      • Job requirements and tags
        • Job required drivers
        • Job pooling
        • Prohibit job combinations
        • Maximum time burden
        • Driver capacity
        • Tags
    • Real-time planning in pick-up and delivery routing
    • Changelog
    • Upgrading to the latest versions
    • Feature requests

Route optimization

By default, Timefold optimizes routes to minimize the amount of time drivers spend driving between stops. This lowers costs, reduces emissions, and makes drivers more productive as they spend less time driving.

1. Minimize travel time

The Minimize travel time soft constraint adds a soft score penalty to the dataset score for every second of travel in a route plan. Timefold is incentivized to use the route plan with the lowest score.

Every soft constraint has a weight that can be configured to change the relative importance of the constraint compared to other constraints.

Learn about constraint weights.

Next

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

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