PyJobShop: Solving scheduling problems with constraint programming in Python
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916620247498752 |
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| author | Lan, Leon Berkhout, Joost |
| author_facet | Lan, Leon Berkhout, Joost |
| contents | This paper presents PyJobShop, an open-source Python library for solving scheduling problems with constraint programming. PyJobShop provides an easy-to-use modeling interface that supports a wide variety of scheduling problems, including well-known variants such as the flexible job shop problem and the resource-constrained project scheduling problem. PyJobShop integrates two state-of-the-art constraint programming solvers: Google's OR-Tools CP-SAT and IBM ILOG's CP Optimizer. We leverage PyJobShop to conduct large-scale numerical experiments on more than 9,000 benchmark instances from the machine scheduling and project scheduling literature, comparing the performance of OR-Tools and CP Optimizer. While CP Optimizer performs better on permutation scheduling and large-scale problems, OR-Tools is highly competitive on job shop scheduling and project scheduling problems--while also being fully open-source. By providing an accessible and tested implementation of constraint programming for scheduling, we hope that PyJobShop will enable researchers and practitioners to use constraint programming for real-world scheduling problems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_13483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PyJobShop: Solving scheduling problems with constraint programming in Python Lan, Leon Berkhout, Joost Optimization and Control This paper presents PyJobShop, an open-source Python library for solving scheduling problems with constraint programming. PyJobShop provides an easy-to-use modeling interface that supports a wide variety of scheduling problems, including well-known variants such as the flexible job shop problem and the resource-constrained project scheduling problem. PyJobShop integrates two state-of-the-art constraint programming solvers: Google's OR-Tools CP-SAT and IBM ILOG's CP Optimizer. We leverage PyJobShop to conduct large-scale numerical experiments on more than 9,000 benchmark instances from the machine scheduling and project scheduling literature, comparing the performance of OR-Tools and CP Optimizer. While CP Optimizer performs better on permutation scheduling and large-scale problems, OR-Tools is highly competitive on job shop scheduling and project scheduling problems--while also being fully open-source. By providing an accessible and tested implementation of constraint programming for scheduling, we hope that PyJobShop will enable researchers and practitioners to use constraint programming for real-world scheduling problems. |
| title | PyJobShop: Solving scheduling problems with constraint programming in Python |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2502.13483 |