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An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem

2022-02Journal article Restricted access
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dc.contributor.authorLuo, Jingyu
dc.contributor.authorVanhoucke, Mario
dc.contributor.authorCoelho, José
dc.contributor.authorGuo, Weikang
dc.date.accessioned2022-11-18T10:32:39Z
dc.date.available2022-11-18T10:32:39Z
dc.date.issued2022-02
dc.description.abstractIn recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLuo, J., Vanhoucke, M., Coelho, J., & Guo, W. (2022). An efficient genetic programming approach to design priority rules for resource-constrained project scheduling problem. Expert Systems with Applications, 198. https://doi.org/10.1016/j.eswa.2022.116753pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2022.116753pt_PT
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10400.2/12602
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherELSEVIERpt_PT
dc.subjectResource-constrained project schedulingpt_PT
dc.subjectPriority rulespt_PT
dc.subjectGenetic programmingpt_PT
dc.titleAn efficient genetic programming approach to design priority rules for resource-constrained project scheduling problempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage20pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleExpert Systems with Applicationspt_PT
oaire.citation.volume198pt_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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