Improving heuristic estimations with constraint propagation in searching for optimal schedules
We face the Job Shop Scheduling Problem by means of branch and bound and A ∗ search. Our main contribution is a new method, based on constraint propagation rules, that allows improving the heuristic estimations. We report results from an experimental study across conventional instances with differen...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universidad de Oviedo (UNIOVI) |
| Repositorio: | RUO. Repositorio Institucional de la Universidad de Oviedo |
| Idioma: | inglés |
| OAI Identifier: | oai:digibuo.uniovi.es:10651/34022 |
| Acceso en línea: | http://hdl.handle.net/10651/34022 |
| Access Level: | acceso abierto |
| Palabra clave: | Job shop scheduling Heuristic search A* algorithm Branch and bound Constraint propagation |
| Sumario: | We face the Job Shop Scheduling Problem by means of branch and bound and A ∗ search. Our main contribution is a new method, based on constraint propagation rules, that allows improving the heuristic estimations. We report results from an experimental study across conventional instances with different sizes showing that A ∗ takes profit from the improved estimations. Both algorithms can reach optimal solutions for medium size instances and, in this case, the branch and bound algorithm is better than A ∗ . However, for very large instances that remain unsolved in both cases, A ∗ returns much better lower bounds due to the improved estimation |
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