#4312 Google OR-Tools

#4312 Google OR-Tools

https://developers.google.com/optimization/introduction

https://developers.google.com/optimization/introduction/python

https://developers.google.com/optimization/scheduling/employee_scheduling

https://developers.google.com/optimization/scheduling/job_shop

#4312 What is the input format of data for Google OR-Tools for optimizing schedules with dependencies and constraints?

Google OR-Tools is a versatile library, but it doesn’t use a single "file format" (like a CSV or JSON schema) by default. Instead, the input format is defined by how you structure your data within your chosen programming language (Python, C++, Java, or .NET) to interface with the CP-SAT Solver.

For scheduling with dependencies and constraints, the most common approach is to model the problem as a Resource-Constrained Project Scheduling Problem (RCPSP).

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1. Data Structure Overview

To feed data into the solver, you typically organize your input into a dictionary or class-based structure containing three primary components:

A. Task/Job Definitions

Each task needs a unique identifier and a duration.

• Format: List of objects or dictionaries.

• Fields: task_id, duration, resource_requirements.

B. Temporal Constraints (Dependencies)

These define the "logic" of the schedule (e.g., Task A must finish before Task B starts).

• Format: A list of tuples or a successor mapping.

• Example: (task_a, task_b) implies End_A <= Start_B.

C. Resource Constraints

Definitions of available "tools" or "people" and their capacities.

• Format: Dictionary mapping resource IDs to their maximum capacity.

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2. Standardized Input Example (Python)

While you can build the model manually, many developers use a structure similar to the PSPLIB (Project Scheduling Problem Library) format. Here is how that looks conceptually in code:

data = {
    "tasks": [
        {"id": 0, "duration": 3, "needs": {"manpower": 1}},
        {"id": 1, "duration": 5, "needs": {"manpower": 2}},
        {"id": 2, "duration": 2, "needs": {"manpower": 1}},
    ],
    "dependencies": [
        (0, 1), # Task 0 must finish before Task 1 starts
        (0, 2), # Task 0 must finish before Task 2 starts
    ],
    "resources": {
        "manpower": 3 # Total available units of this resource
    },
    "horizon": 20 # Total maximum time allowed for the project
}

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3. How the Data is "Inputted" into the Solver

OR-Tools doesn't "read" the dict directly; you map this data to Interval Variables. This is the "internal format" the solver actually works with:

• NewIntervalVar(start, duration, end, name): This creates a constraint where $start + duration = end$.

• Add(start_b >= end_a): This is how you input a dependency.

• AddCumulative(intervals, demands, capacity): This is how you input resource constraints.

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4. Supported External Formats

If you are looking for a specific file extension to load, OR-Tools provides utilities in its scheduling_20 protobuf library to parse:

Format	Description
.rcp / .sm	Standard RCPSP files used in academic research (PSPLIB).
Protocol Buffers	You can define your input in a .proto file, which is Google’s preferred way to serialize structured data.
JSON	Easily parsed into the Python dictionary structure shown above.

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5. Pro-Tip: The "Horizon"

A common mistake in the input data is forgetting the Horizon. The solver needs an upper bound for time. Usually, you calculate this as the sum of all task durations:

Horizon} = sum{i in Tasks} duration_i

If your input data doesn't include a maximum timeframe, your solver variables won't have a defined range to search within.

How complex are the dependencies you're working with—are we talking simple finish-to-start, or do you have more "fun" ones like overlapping lags and lead times?

From <https://gemini.google.com/app/>  Google Gemini 3.0 (Fast)