Abstract
The job shop scheduling problem (JSSP) is an abstraction of industrial scheduling and has been studied since the dawn of the computer era. Its combinatorial nature makes it easily expressible as a constraint satisfaction problem. Nevertheless, in the last decade, there has been a hiatus in the research on this topic from the constraint community; even when this problem is addressed, the target instances are from benchmarks that are more than 20 years old. And yet, constraint solvers have continued to evolve and the standards of today’s industry have drastically changed. Our aim is to close this research gap by testing the capabilities of the best available CP solvers on the JSSP. We target not only the classic benchmarks from the literature but also a new benchmark of large-scale instances reflecting nowadays industrial scenarios. Furthermore, we analyze different encodings of the JSSP to measure the impact of high-level structures (such as interval variables and no-overlap constraints) on the problem solution. The solvers considered are OR-Tools, Google’s open-source solver and winner of the last MiniZinc Challenge, and IBM’s CP Optimizer, a proprietary solver targeted towards industrial scheduling problems.
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Notes
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complete encodings and benchmarks are available at https://goo.gl/qarP3m.
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With the exception of ORT SemiNaive.
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Da Col, G., Teppan, E.C. (2019). Industrial Size Job Shop Scheduling Tackled by Present Day CP Solvers. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_9
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