up-cpse 0.1.0

Constraint Programming Scheduling Engine

CPSE

CPSE (Constraint Programming Scheduling Engine) is a scheduling engine that encodes scheduling problems as constraint satisfaction models and solves them using the CP-SAT solver from Google OR-Tools.

CPSE offers two scheduling engines, each supporting different problem kinds:

• cpse: Supports scheduling problems with optional activities and scoped constraints. Partial support for fluents is provided: only increase and decrease effects on non-parametric fluents are handled, while assignment effects are not supported.

• cpse-timepoints: Supports scheduling problems without optional activities. Offers full support for fluents, but is generally more computationally intensive.

Installation

pip install up-cpse

To try the latest unreleased build, install a wheel directly from the rolling dev pre-release:

pip install --pre <url-of-wheel-on-dev-release>

Usage

CPSE is fully integrated with the Unified Planning framework. Before using CPSE, register its engines in the Unified Planning environment:

from unified_planning.shortcuts import *

# Register CPSE engines
env = get_environment()
env.factory.add_engine("cpse", "cpse", "CPSE")
env.factory.add_engine("cpse-timepoints", "cpse", "CPSETimepoints")

# Define your scheduling problem
scheduling_problem = ...

# Solve the problem using the cpse engine
with OneshotPlanner(name="cpse") as planner:
    result = planner.solve(scheduling_problem)
    print(result.plan)

Parameters

The CPSE engines support the following configuration parameters:

Parameter	Type	Default Value	Description
lower_bound	int	0	Minimum value for all model variables if not explicitly specified in the problem.
upper_bound	int	INT32_MAX	Maximum value for all model variables if not explicitly specified in the problem (INT32_MAX = 2^31 - 1).

These parameters can be passed as a dictionary to OneshotPlanner:

params = {
  "lower_bound": 1,
  "upper_bound": 100
}

with OneshotPlanner(name="cpse", params=params) as planner:
    result = planner.solve(scheduling_problem)
    print(result.plan)

Tip: Adjusting these bounds can help restrict variable domains or improve solver performance for specific scheduling problems.

Development

CPSE uses uv to manage the environment and just as a task runner. After cloning:

just install        # uv sync — create .venv from uv.lock

Common tasks:

just test           # run the pytest suite
just lint           # ruff lint + format checks
just format         # auto-fix lint issues and format
just typecheck      # mypy
just precommit      # run all pre-commit hooks against the whole repo
just build          # build sdist + wheel into ./dist/

Install the git hook so the checks run automatically on each commit:

uv run pre-commit install

Running just --list shows all available recipes.

References

CPSE has been used in the following research paper:

• Elisa Tosello, Arthur Bit-Monnot, Davide Lusuardi, Alessandro Valentini and Andrea Micheli (2026). Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions. ICAPS 2026

License

CPSE is released under the GNU General Public License v3.0 (GPL-3.0). See the LICENSE file for full details.

Contact

For questions, bug reports, or contributions, please open an issue on GitHub or contact the authors at [e-mail].