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dc.contributor.authorGuo, Weikang
dc.contributor.authorVanhoucke, Mario
dc.contributor.authorCoelho, José
dc.date.accessioned2023-01-16T07:47:43Z
dc.date.available2023-01-16T07:47:43Z
dc.date.issued2023en_US
dc.identifier.issn0377-2217
dc.identifier.doi10.1016/j.ejor.2022.08.042
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7158
dc.description.abstractThe branch-and-bound (B&B) procedure is one of the most widely used techniques to get optimal solutions for the resource-constrained project scheduling problem (RCPSP). Recently, various components from the literature have been assembled by Coelho and Vanhoucke (2018) into a unified search algorithm using the best performing lower bounds, branching schemes, search strategies, and dominance rules. However, due to the high computational time, this procedure is only suitable to solve small to medium-sized problems. Moreover, despite its relatively good performance, not much is known about which components perform best, and how these components should be combined into a procedure to maximize chances to solve the problem. This paper introduces a structured prediction approach to rank various combinations of components (configurations) of the integrated B&B procedure. More specifically, two regression methods are used to map project indicators to a full ranking of configurations. The objective is to provide preference information about the quality of different configurations to obtain the best possible solution. Using such models, the ranking of all configurations can be predicted, and these predictions are then used to get the best possible solution for a new project with known network and resource values. A computational experiment is conducted to verify the performance of this novel approach. Furthermore, the models are tested for 48 different configurations, and their robustness is investigated on datasets with different numbers of activities. The results show that the two models are very competitive, and both can generate significantly better results than any single-best configuration.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectProject Schedulingen_US
dc.subjectRCPSPen_US
dc.subjectPreference Learningen_US
dc.subjectLabel Rankingen_US
dc.subjectPerformance Predictionen_US
dc.titleA prediction model for ranking branch-and-bound procedures for the resource-constrained project scheduling problemen_US
dc.identifier.journalEuropean Journal of Operational Researchen_US
dc.source.volume306en_US
dc.source.issue2en_US
dc.source.beginpage579en_US
dc.source.endpage595en_US
dc.contributor.departmentFaculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, Gent 9000, Belgiumen_US
dc.contributor.departmentUCL School of Management, University College London, 1 Canada Square, London E14 5AA, United Kingdomen_US
dc.contributor.departmentINESC - Technology and Science, Porto (Portugal) and Universidade Aberta, Rua da Escola Politécnica, 147, Lisbon, 1269-001, Portugalen_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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