2027. (Sim)PyJobShop: Integrating Optimization and Simulation for Uncertainty-Aware Scheduling
Invited abstract in session TD-12: Job shop scheduling, stream Scheduling and Project Management.
Tuesday, 14:30-16:00Room: Clarendon SR 1.02
Authors (first author is the speaker)
| 1. | Joost Berkhout
|
| Vrije Universiteit Amsterdam | |
| 2. | Leon Lan
|
| Vrije Universiteit Amsterdam |
Abstract
This talk introduces PyJobShop, an open-source Python library for modeling and solving scheduling problems, and SimPyJobShop, an extension integrating discrete-event simulation to account for uncertainty.
PyJobShop offers a flexible interface for various scheduling problems, including job shop and resource-constrained project scheduling. It integrates two constraint programming solvers: the open-source OR-Tools CP-SAT and the commercial IBM ILOG CP Optimizer. Large-scale experiments on 9,000+ benchmark instances show that PyJobShop identifies new best-known solutions. Additionally, CP-SAT performs competitively with CP Optimizer on most scheduling problems, though CP Optimizer scales better for large and permutation-based instances.
SimPyJobShop extends PyJobShop by enabling uncertainty modeling through a simheuristics framework. We evaluate multiple simheuristic strategies across diverse scheduling scenarios, demonstrating how SimPyJobShop facilitates uncertainty-aware decision-making with minimal additional modeling effort. By unifying optimization and simulation, we provide researchers and practitioners with a powerful tool for improving scheduling solutions under uncertainty.
Keywords
- Scheduling
- Stochastic Optimization
- Simulation
Status: accepted
Back to the list of papers