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A prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problem

2022-09-01Journal article Restricted access
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datacite.subject.sdg09:Indústria, Inovação e Infraestruturaspt_PT
datacite.subject.sdg12:Produção e Consumo Sustentáveispt_PT
dc.contributor.authorGuo, Weikang
dc.contributor.authorVanhoucke, Mario
dc.contributor.authorCoelho, José
dc.date.accessioned2024-10-25T09:06:55Z
dc.date.available2024-10-25T09:06:55Z
dc.date.issued2022-09-01
dc.description.abstractThe branch-and-bound (B&B) procedure is one of the most widely used techniques to get optimal solutions for the resource-constrained project scheduling problem (RCPSP). Recently, various components from the literature have been assembled by Coelho and Vanhoucke (2018) into a unified search algorithm using the best performing lower bounds, branching schemes, search strategies, and dominance rules. However, due to the high computational time, this procedure is only suitable to solve small to medium-sized problems. Moreover, despite its relatively good performance, not much is known about which components perform best, and how these components should be combined into a procedure to maximize chances to solve the problem. This paper introduces a structured prediction approach to rank various combinations of components (configurations) of the integrated B&B procedure. More specifically, two regression methods are used to map project indicators to a full ranking of configurations. The objective is to provide preference information about the quality of different configurations to obtain the best possible solution. Using such models, the ranking of all configurations can be predicted, and these predictions are then used to get the best possible solution for a new project with known network and resource values. A computational experiment is conducted to verify the performance of this novel approach. Furthermore, the models are tested for 48 different configurations, and their robustness is investigated on datasets with different numbers of activities. The results show that the two models are very competitive, and both can generate significantly better results than any single-best configuration.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.ejor.2022.08.042pt_PT
dc.identifier.issn0377-2217
dc.identifier.urihttp://hdl.handle.net/10400.2/16683
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.subjectProject schedulingpt_PT
dc.subjectRCPSPpt_PT
dc.subjectPreference learningpt_PT
dc.subjectLabel rankingpt_PT
dc.subjectPerformance predictionpt_PT
dc.subjectInstance complexitypt_PT
dc.titleA prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage595pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage579pt_PT
oaire.citation.titleEuropean Journal of Operational Researchpt_PT
oaire.citation.volume306pt_PT
person.familyNameVanhoucke
person.familyNameCoelho
person.givenNameMario
person.givenNameJosé
person.identifierR-000-8V7
person.identifier.ciencia-id7D18-9842-159F
person.identifier.orcid0000-0001-6702-3563
person.identifier.orcid0000-0002-5855-284X
person.identifier.ridD-8647-2015
person.identifier.scopus-author-id6507772652
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication129fc49c-d742-406a-b680-f5544f8da0e2
relation.isAuthorOfPublication2926ed15-fe04-4ee4-a40d-ad0a83e33af8
relation.isAuthorOfPublication.latestForDiscovery129fc49c-d742-406a-b680-f5544f8da0e2

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