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dc.contributor.authorSnauwaert, Jakob
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2022-06-13T14:22:41Z
dc.date.available2022-06-13T14:22:41Z
dc.date.issued2022en_US
dc.identifier.issn0377-2217
dc.identifier.doi10.1016/j.ejor.2022.05.049
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7052
dc.description.abstractThis paper studies and analyses the multi-skilled resource-constrained project scheduling problem (MSRCPSP). We present a new classification scheme based on an existing classification scheme for project scheduling problems. This allows researchers to classify all multi-skilled project scheduling problems and its extensions. Furthermore, we propose a new data generation procedure for the MSRCPSP and introduce multiple artificial datasets for varying research purposes. The new datasets are generated based on new multi-skilled resource parameters and are compared to existing benchmark datasets in the literature. A set of 7 empirical multi-skilled project instances from software and railway construction companies are collected in order to validate the quality of the artificial datasets. Solutions are obtained through a genetic algorithm and by solving a mixed-integer linear programming formulation with CPLEX 12.6. The hardness of the multi-skilled project instances is investigated in the computational experiments. An experimental analysis studies the impact of skill availability, workforce size and multi-skilling on the makespan of the project.en_US
dc.description.sponsorshipWe acknowledge the support provided by the Nationale Bank van Belgi (NBB) and the Bijzonder Onderzoeksfonds (BOF) for the project, under contract number BOF12GOA021. The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO and the Flemish Government - department EWI.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectProject Schedulingen_US
dc.subjectResource-Constrained Schedulingen_US
dc.subjectSkillsen_US
dc.titleA classification and new benchmark instances for the multi-skilled resource-constrained project scheduling problemen_US
dc.identifier.journalEuropean Journal of Operational Researchen_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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