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hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorSADYKOV, Ruslan
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorVANDERBECK, François
hal.structure.identifierUniversidade Federal Fluminense [Rio de Janeiro] [UFF]
dc.contributor.authorPESSOA, Artur
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorTAHIRI, Issam
hal.structure.identifierUniversidade Federal Fluminense [Rio de Janeiro] [UFF]
dc.contributor.authorUCHOA, Eduardo
dc.date.accessioned2024-04-04T03:10:19Z
dc.date.available2024-04-04T03:10:19Z
dc.date.issued2019
dc.identifier.issn1091-9856
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193679
dc.description.abstractEnPrimal heuristics have become an essential component in mixed integer programming (MIP) solvers. Extending MIP based heuristics, our study outlines generic procedures to build primal solutions in the context of a branch-and-price approach and reports on their performance. Our heuristic decisions carry on variables of the Dantzig-Wolfe reformulation, the motivation being to take advantage of a tighter linear programming relaxation than that of the original compact formulation and to benefit from the combinatorial structure embedded in these variables. We focus on the so-called diving methods that use re-optimization after each LP rounding. We explore combinations with diversification- intensification paradigms such as limited discrepancy search , sub-MIPing, local branching, and strong branching. The dynamic generation of variables inherent to a column generation approach requires specific adaptation of heuristic paradigms. We manage to use simple strategies to get around these technical issues. Our numerical results on generalized assignment, cutting stock, and vertex coloring problems sets new benchmarks, highlighting the performance of diving heuristics as generic procedures in a column generation context and producing better solutions than state-of-the-art specialized heuristics in some cases.
dc.language.isoen
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)
dc.subject.enVertex Coloring
dc.subject.enCutting Stock
dc.subject.enMathheuristics
dc.subject.enDiving Heuristics
dc.subject.enGeneralized Assignment
dc.subject.enColumn Generation
dc.subject.enPrimal Heuristics
dc.subject.enBranch-and-Price
dc.subject.enRounding Heuristics
dc.title.enPrimal Heuristics for Branch-and-Price: the assets of diving methods
dc.typeArticle de revue
dc.identifier.doi10.1287/ijoc.2018.0822
dc.subject.halInformatique [cs]/Recherche opérationnelle [cs.RO]
bordeaux.journalINFORMS Journal on Computing
bordeaux.page251-267
bordeaux.volume31
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01237204
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01237204v1
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