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Pattern based diving heuristics for a two-dimensional guillotine cutting-stock problem with leftovers

hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierUniversité de Bordeaux [UB]
dc.contributor.authorCLAUTIAUX, François
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorSADYKOV, Ruslan
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
hal.structure.identifierUniversité de Bordeaux [UB]
dc.contributor.authorVANDERBECK, François
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierReformulations based algorithms for Combinatorial Optimization [Realopt]
dc.contributor.authorVIAUD, Quentin
dc.date.accessioned2024-04-04T03:07:45Z
dc.date.available2024-04-04T03:07:45Z
dc.date.issued2019-09
dc.identifier.issn2192-4406
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193473
dc.description.abstractEnWe consider a variant of two-dimensional guillotine cutting-stock problem that arises when different bills of order (or batches) are considered consecutively. The raw material leftover of the last cutting pattern is not counted as waste as it can be reused for cutting the next batch. The objective is thus to maximize the length of the leftover. We propose a diving heuristic based on a Dantzig-Wolfe reformulation solved by column generation in which the pricing problem is solved using dynamic programming (DP). This DP generates so-called non-proper columns, i.e. cutting patterns that cannot participate in a feasible integer solution of the problem. We show how to adapt the standard diving heuristic to this " non-proper " case while keeping its effectiveness. We also introduce the partial enumeration technique, which is designed to reduce the number of non-proper patterns in the solution space of the dynamic program. This technique helps to strengthen the lower bounds obtained by column generation and improve the quality of solutions found by the diving heuristic. Computational results are reported and compared on classical benchmarks from the literature as well as on new instances inspired from industrial data. According to these results, proposed diving algorithms outperform constructive and evolutionary heuristics.
dc.language.isoen
dc.publisherSpringer
dc.subject.enCutting and Packing
dc.subject.enDiving Heuristic
dc.subject.enDynamic Programming
dc.title.enPattern based diving heuristics for a two-dimensional guillotine cutting-stock problem with leftovers
dc.typeArticle de revue
dc.identifier.doi10.1007/s13675-019-00113-9
dc.subject.halInformatique [cs]/Recherche opérationnelle [cs.RO]
bordeaux.journalEURO Journal on Computational Optimization
bordeaux.page265–297
bordeaux.volume7
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue3
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01656179
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01656179v1
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