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dc.contributor.authorDebels, Dieter*
dc.contributor.authorVanhoucke, Mario*
dc.date.accessioned2017-12-02T14:17:32Z
dc.date.available2017-12-02T14:17:32Z
dc.date.issued2005
dc.identifier.urihttp://hdl.handle.net/20.500.12127/1685
dc.description.abstractThe resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward local search scheduling technique. Comparative computational results reveal that this procedure can be considered as today's best performing RCPSP heuristic.
dc.language.isoen
dc.subjectProgramme & Portfolio Management
dc.titleA Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem
dc.source.issue8
dc.source.numberofpages22
vlerick.knowledgedomainOperations & Supply Chain Management
vlerick.supervisor
vlerick.typecommWorking paper
vlerick.vlerickdepartmentTOM
dc.relation.urlhttp://public.vlerick.com/Publications/b3fb92cf-69a9-e011-8a89-005056a635ed.pdf
dc.identifier.vperid117045
dc.identifier.vperid58614
dc.identifier.vpubid1836


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