PyCSP3-Scheduling: A Scheduling Extension for PyCSP3
For constraint programming practitioners, this provides a higher-level scheduling modeling interface within the PyCSP3 ecosystem, but the contribution is incremental as it compiles to existing constraints.
PyCSP3 lacked native scheduling abstractions, requiring low-level encodings. The authors introduced PyCSP3 Scheduling, a library with 53 constraints and 27 expressions that compile to standard PyCSP3/XCSP3, achieving identical objectives on 72 doubly-proved optimal pairs and up to 5.8x speedups on some families, though with regressions on others.
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