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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

Model output

The modelOutput object of the output dataset includes machines, employees and unassignedJobs.

In the following example, Machine 1 is assigned to Job A, Ann is assigned to Job B, and Job C is unassigned:

{
  "modelOutput": {
    "machines": [
      {
        "id": "Machine 1",
        "schedule": [
          {
            "id": "Job A",
            "start": "2027-02-01T09:00:00-04:00",
            "end": "2027-02-01T17:00:00-04:00"
          }
        ]
      }
    ],
    "employees": [
      {
        "id": "Ann",
        "schedule": [
          {
            "id": "Job B",
            "start": "2027-02-01T09:00:00-04:00",
            "end": "2027-02-01T17:00:00-04:00"
          }
        ]
      }
    ],
    "unassignedJobs": [
      {
        "id": "Job C"
      }
    ]
  }
}

Next

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

  • Learn about Input metrics.

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