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  • Pick-up and Delivery Routing
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  • Metrics and optimization goals

Pick-up and Delivery Routing

    • Introduction
    • Getting started: Hello world
    • User guide
      • Terms
      • Planning AI concepts
      • 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 stops and optional stops
      • Job requirements and tags
        • Job required drivers
        • Job pooling
        • Prohibit job combinations
        • Maximum time burden
        • Driver capacity
        • Tags
    • Changelog
    • Upgrading to the latest versions
    • Feature requests

Metrics and optimization goals

Why metrics matter

Timefold Platform helps you solve complex planning problems by searching for the best possible solution given your constraints. But how do you determine if one solution is better than another?

Each generated schedule is assigned a score based on hard, medium, and soft constraints. However, this score is primarily used by the solver to guide optimization and helps identify the impact of different constraints, but it doesn’t have a meaningful interpretation in your day-to-day operations. Read more about constraints and constraint scores.

That’s why the Timefold Platform provides a set of metrics that help you evaluate and compare different scheduling solutions in a way that aligns with your business objectives.

We continuously update and refine available metrics. You can view the most up-to-date list in the Timefold Platform UI or check the OpenAPI specifications.

Where to find metrics

You can access and analyze metrics in multiple places within Timefold Platform:

  • Dataset Detail Page: The sidebar displays five top-level metrics, with an option to expand and see all available metrics.

  • Plans Overview Table: Customize which metrics appear in the table to compare solutions across multiple datasets. Use tagging and filtering features to focus on certain segments.

  • Dataset Output: Metrics are included as part of the JSON output file for deeper analysis.

Goal Alignment

Real-world scheduling is a balancing act between multiple competing objectives. Optimization is not a zero-sum game, improving one objective doesn’t always mean sacrificing another. Instead, constraint weights can be tuned to prioritize different goals. Metrics help you choose between different datasets with different optimization objectives.

Using configuration profiles for goal alignment

Our platform supports configuration profiles that allow you to define your objectives. This feature allows you to:

  • Compare how different priorities impact the final solution.

  • Identify solutions that balance objectives effectively.

  • Adjust trade-offs dynamically based on evolving business needs.

By using metrics and configuration profiles together, you can gain deeper insights into your scheduling challenges and ensure your optimization efforts align with your strategic goals.

Read more about configuration profiles in the Timefold Platform.

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