Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem

Giacomo Da Col
Erich Teppan

The job-shop scheduling is one of the most studied optimization problems from the dawn of computer era to the present day. Its combinatorial nature makes it easily expressible as a constraint satisfaction problem. In this paper, we compare the performance of two constraint solvers on the job-shop scheduling problem. The solvers in question are: OR-Tools, an open-source solver developed by Google and winner of the last MiniZinc Challenge, and CP Optimizer, a proprietary IBM constraint solver targeted at industrial scheduling problems. The comparison is based on the goodness of the solutions found and the time required to solve the problem instances. First, we target the classic benchmarks from the literature, then we carry out the comparison on a benchmark that was created with known optimal solution, with size comparable to real-world industrial problems.

In Bart Bogaerts, Esra Erdem, Paul Fodor, Andrea Formisano, Giovambattista Ianni, Daniela Inclezan, German Vidal, Alicia Villanueva, Marina De Vos and Fangkai Yang: Proceedings 35th International Conference on Logic Programming (Technical Communications) (ICLP 2019), Las Cruces, NM, USA, September 20-25, 2019, Electronic Proceedings in Theoretical Computer Science 306, pp. 259–265.
Published: 19th September 2019.

ArXived at: https://dx.doi.org/10.4204/EPTCS.306.30 bibtex PDF
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