cs.AIMay 14, 2026

PyCSP3-Scheduling: A Scheduling Extension for PyCSP3

Authors: Sohaib Afifi

Organizations: Univ. Artois, UR 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), F-62400 Béthune, France

Abstract

PyCSP3^3 provides a productive way to build constraint models for solving combinatorial constrained problems and export them to XCSP3^3, 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 PyCSP3^3 already provides global constraints like NoOverlap and Cumulative on integer arrays. We present PyCSP3^3 Scheduling, a library that adds scheduling abstractions to PyCSP3^3 through 53 dedicated constraints and 27 expressions, and compiles them down to standard PyCSP3^3/XCSP3^3 constraints, maintaining the modeling/solving separation that underpins the PyCSP3^3 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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