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Task Scheduling

    • Introduction
    • Getting started: Hello world
    • User guide
      • Terminology
      • Scheduling API concepts
      • Integration
      • Constraints
      • Using the API
        • Using the OpenAPI spec
        • API tooling
      • Demo datasets
      • Input datasets
        • Model configuration
        • Model input
      • Output datasets
        • Metadata
        • Model output
        • Input metrics
        • Key performance indicators (KPIs)
      • Job types and machine types
      • Resource-specific durations
      • Freeze jobs until
      • Metrics and optimization goals
      • Score analysis
      • Validation
    • Machine and employee resource constraints
      • Machine unavailability
      • Resource transitions
      • Employee resources
    • Job service constraints
      • Time windows
      • Time management
      • Job dependencies
      • Priority jobs
      • Tags and specific resources
    • Real-time planning
    • Changelog
    • Upgrading to the latest versions
    • Feature requests

Input metrics

The inputMetrics object includes metrics that are calculated from data included in the input dataset. This can be useful for troubleshooting unexpected results by making sure that the inputs, for instance, number of employees, matches the number of employees that were supposed to be included in the input dataset.

{
  "inputMetrics": {
    "jobs": 4064,
    "machines": 100,
    "employees": 50
  }
}

Next

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

  • Learn about the Key performance indicators (KPIs).

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