PyCSP3-Scheduling: A Scheduling Extension for PyCSP3

• URL: http://arxiv.org/abs/2605.14559v1
• Date: Thu, 14 May 2026 08:33:34 GMT
• Title: PyCSP3-Scheduling: A Scheduling Extension for PyCSP3
• Abstract summary: We present PyCSP$3$ Scheduling, a library that adds scheduling abstractions to PyCSP$3$.<n>On 261 paired instances across 17 model families, both formulations produce identical objectives on all 72 doubly-proved optimal pairs.<n> runtime performance diverges across families, with clear gains on some (up to 5.8x) and regressions on others.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PyCSP$^3$ provides a productive way to build constraint models for solving combinatorial constrained problems and export them to XCSP$^3$, preserving a complete separation between modeling and solving. However, it lacks native support for scheduling abstractions such as interval variables, sequence variables, and resource functions. As a result, scheduling models must be encoded with low-level integer variables and manual channeling constraints, even though PyCSP$^3$ already provides global constraints like NoOverlap and Cumulative on integer arrays. We present PyCSP$^3$ Scheduling, a library that adds scheduling abstractions to PyCSP$^3$ through 53 dedicated constraints and 27 expressions, and compiles them down to standard PyCSP$^3$/XCSP$^3$ constraints, maintaining the modeling/solving separation that underpins the PyCSP$^3$ ecosystem. On 261 paired instances across 17 model families (5 runs each), both formulations produce identical objectives on all 72 doubly-proved optimal pairs and nearly half of the families (8/17) remain structurally unchanged after compilation; however, runtime performance diverges across families, with clear gains on some (up to 5.8x) and regressions on others due to the overhead of compilation decompositions. Code and benchmarks are available at: https://github.com/sohaibafifi/pycsp3-scheduling

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