http://www.cnr.it/ontology/cnr/individuo/prodotto/ID201352
Solving Job Shop Scheduling with Setup Times through Constraint-based Iterative Sampling: an Experimental Analysis (Articolo in rivista)
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- Solving Job Shop Scheduling with Setup Times through Constraint-based Iterative Sampling: an Experimental Analysis (Articolo in rivista) (literal)
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- 2011-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/s10472-011-9264-8 (literal)
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Oddi, Angelo ; Rasconi, Riccardo ; Cesta, Amedeo ; Smith, Stephen F. (2011)
Solving Job Shop Scheduling with Setup Times through Constraint-based Iterative Sampling: an Experimental Analysis
in Annals of mathematics and artificial intelligence; Springer, Berlin (Germania)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Oddi, Angelo ; Rasconi, Riccardo ; Cesta, Amedeo ; Smith, Stephen F. (literal)
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- ID_PUMA: /cnr.istc/2011-A0-062. - Rivista pubblicata anche online (ISSN 1573-7470). - Area di valutazione 09 - Ingegneria industriale e informatica (literal)
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- CNR-ISTC, Roma1-SanMartino ; CMU - Carnegie Mellon University, Robotics Institute (literal)
- Titolo
- Solving Job Shop Scheduling with Setup Times through Constraint-based Iterative Sampling: an Experimental Analysis (literal)
- Abstract
- This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/max). The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints. (literal)
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