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dc.contributor.authorDebels, Dieter
dc.contributor.authorVanhoucke, Mario
dc.date.accessioned2017-12-02T14:23:32Z
dc.date.available2017-12-02T14:23:32Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/20.500.12127/1986
dc.description.abstractThe resource-constrained project scheduling problem (RCPSP) has been extensively investigated during the past decades. Due to its strongly NP-hard status and the need for solving large realistic project instances, the recent focus has shifted from exact optimisation procedures to (meta-) heuristic approaches. In this paper, we extend some existing state-of-the-art RCPSP procedures in two ways. First, we extensively test a decomposition approach that splits problem instances into smaller sub-problems to be solved with an (exact or heuristic) procedure, and re-incorporates the obtained solutions for the sub-problems into the solution of the main problem, possibly leading to an overall better solution. Second, we study the influence of an extended neighbourhood search on the performance of a meta-heuristic procedure. Computational results reveal that both techniques are valuable extensions and lead to improved results.
dc.language.isoen
dc.subjectProgramme & Portfolio Management
dc.titleMeta-heuristic resource constrained project scheduling: solution space restrictions and neighbourhood extensions
refterms.dateFOA2019-10-14T14:27:09Z
dc.source.issue18
dc.source.numberofpages25
vlerick.knowledgedomainOperations & Supply Chain Management
vlerick.supervisor
vlerick.typecommWorking paper
vlerick.vlerickdepartmentTOM
dc.relation.urlhttp://public.vlerick.com/Publications/4b642c0d-6aa9-e011-8a89-005056a635ed.pdf
dc.identifier.vperid117045
dc.identifier.vperid58614
dc.identifier.vpubid2223


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