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New resource-constrained project scheduling instances for testing (meta-)heuristic scheduling algorithms

2023-02-09Journal 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.authorCoelho, José
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2024-10-25T08:46:05Z
dc.date.available2024-10-25T08:46:05Z
dc.date.issued2023-02-09
dc.description.abstractThe resource-constrained project scheduling problem (RCPSP) is a well-known scheduling problem that has attracted attention since several decades. Despite the rapid progress of exact and (meta-)heuristic procedures, the problem can still not be solved to optimality for many problem instances of relatively small size. Due to the known complexity, many researchers have proposed fast and efficient meta-heuristic solution procedures that can solve the problem to near optimality. Despite the excellent results obtained in the last decades, little is known why some heuristics perform better than others. However, if researchers better understood why some meta-heuristic procedures generate good solutions for some project instances while still falling short for others, this could lead to insights to improve these meta-heuristics, ultimately leading to stronger algorithms and better overall solution quality. In this study, a new hardness indicator is proposed to measure the difficulty of providing near-optimal solutions for meta-heuristic procedures. The new indicator is based on a new concept that uses the 𝜎 distance metric to describe the solution space of the problem instance, and relies on current knowledge for lower and upper bound calculations for problem instances from five known datasets in the literature. This new indicator, which will be called the 𝜎𝐷 indicator, will be used not only to measure the hardness of existing project datasets, but also to generate a new benchmark dataset that can be used for future research purposes. The new dataset contains project instances with different values for the 𝜎𝐷 indicator, and it will be shown that the value of the 𝜎 distance metric actually describes the difficulty of the project instances through two fast and efficient meta-heuristic procedures from the literature.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.cor.2023.106165pt_PT
dc.identifier.issn0305-0548
dc.identifier.urihttp://hdl.handle.net/10400.2/16681
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.subjectHeuristicspt_PT
dc.subjectResource-constrained project schedulingpt_PT
dc.subjectProject networkspt_PT
dc.subjectResource constraintspt_PT
dc.titleNew resource-constrained project scheduling instances for testing (meta-)heuristic scheduling algorithmspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage106165pt_PT
oaire.citation.titleComputers & Operations Researchpt_PT
oaire.citation.volume153pt_PT
person.familyNameCoelho
person.familyNameVanhoucke
person.givenNameJosé
person.givenNameMario
person.identifierR-000-8V7
person.identifier.ciencia-id7D18-9842-159F
person.identifier.orcid0000-0002-5855-284X
person.identifier.orcid0000-0001-6702-3563
person.identifier.ridD-8647-2015
person.identifier.scopus-author-id6507772652
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication2926ed15-fe04-4ee4-a40d-ad0a83e33af8
relation.isAuthorOfPublication129fc49c-d742-406a-b680-f5544f8da0e2
relation.isAuthorOfPublication.latestForDiscovery2926ed15-fe04-4ee4-a40d-ad0a83e33af8

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