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Automatic detection of the best performing priority rule for the resource-constrained project scheduling problem

2021-04Journal article Restricted access
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dc.contributor.authorGuo, Weikang
dc.contributor.authorVanhoucke, Mario
dc.contributor.authorCoelho, José
dc.contributor.authorLuo, Jingyu
dc.date.accessioned2022-11-18T09:49:56Z
dc.date.available2022-11-18T09:49:56Z
dc.date.issued2021-04
dc.description.abstractPriority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGuo, W., Vanhoucke, M., Coelho, J., & Luo, J. (2021). Automatic detection of the best performing priority rule for the resource-constrained project scheduling problem. Expert Systems with Applications, 167, 114116. https://doi.org/10.1016/j.eswa.2020.114116pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2020.114116pt_PT
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10400.2/12599
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherELSEVIERpt_PT
dc.subjectProject schedulingpt_PT
dc.subjectRCPSPpt_PT
dc.subjectMachine learningpt_PT
dc.subjectClassification modelpt_PT
dc.subjectPerformance predictionpt_PT
dc.titleAutomatic detection of the best performing priority rule for the resource-constrained project scheduling problempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage19pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleExpert Systems with Applicationspt_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.latestForDiscovery2926ed15-fe04-4ee4-a40d-ad0a83e33af8

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