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dc.contributor.authorBredael, Dries
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2023-02-24T13:29:56Z
dc.date.available2023-02-24T13:29:56Z
dc.date.issued2023en_US
dc.identifier.issn0377-2217
dc.identifier.doi10.1016/j.ejor.2022.11.009
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7190
dc.description.abstractThis paper reviews a set of ten existing metaheuristic solution procedures for the resource-constrained multi-project scheduling problem. Algorithmic implementations are constructed based on the description of the original procedures in literature. Equivalence is verified on the original test instances for the original objective and parameters through a comparison with the reported results. An extensive benchmark analysis is performed on a novel, publicly available dataset for a variety of optimisation criteria and due date settings for which the original algorithms have not been tested earlier. The impact of the different objectives, due dates and test instance parameters is analysed and an overall ranking of the metaheuristic solution methods for different situations is discussed. Key insights into the structure of competitive solutions for disparate objectives and due date settings are presented and effective algorithmic components are revealed.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectProject Schedulingen_US
dc.subjectMulti-Projecten_US
dc.subjectBenchmark Analysisen_US
dc.subjectMetaheuristicsen_US
dc.titleMulti-project scheduling: A benchmark analysis of metaheuristic algorithms on various optimisation criteria and due datesen_US
dc.identifier.journalEuropean Journal of Operational Researchen_US
dc.source.volume308en_US
dc.source.issue1en_US
dc.source.beginpage54en_US
dc.source.endpage75en_US
dc.contributor.departmentFaculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, Ghent 9000, Belgiumen_US
dc.contributor.departmentUCL School of Management, University College London, 1 Canada Square, London E14 5AA UKen_US
dc.identifier.eissn1872-6860
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleVlerick strategic journal articleen_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid58614en_US


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