Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104192
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorFu, Xen_US
dc.creatorChan, FTSen_US
dc.creatorNiu, Ben_US
dc.creatorChung, NSHen_US
dc.creatorQu, Ten_US
dc.date.accessioned2024-02-05T08:47:01Z-
dc.date.available2024-02-05T08:47:01Z-
dc.identifier.issn2210-6502en_US
dc.identifier.urihttp://hdl.handle.net/10397/104192-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2019 Published by Elsevier B.V.en_US
dc.rights© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Fu, X., Chan, F. T. S., Niu, B., Chung, N. S. H., & Qu, T. (2019). A three-level particle swarm optimization with variable neighbourhood search algorithm for the production scheduling problem with mould maintenance. Swarm and Evolutionary Computation, 50, 100572 is available at https://doi.org/10.1016/j.swevo.2019.100572.en_US
dc.subjectMachine maintenanceen_US
dc.subjectMould maintenanceen_US
dc.subjectProduction schedulingen_US
dc.subjectThree-level particle swarm optimizationen_US
dc.subjectVariable neighbourhood searchen_US
dc.titleA three-level particle swarm optimization with variable neighbourhood search algorithm for the production scheduling problem with mould maintenanceen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume50en_US
dc.identifier.doi10.1016/j.swevo.2019.100572en_US
dcterms.abstractTo improve the reliability of production systems in the plastics industry, researchers are now taking mould maintenance into consideration, besides machine maintenance, in the production scheduling problem. Different strategies and approaches have been proposed to solve the production scheduling with mould maintenance (PS-MM) problem. However, it remains a challenge to provide a satisfactory solution. In this research, a new hybrid metaheuristic algorithm (TLPSO-VNS algorithm) is proposed, which is a combination of the three-level particle swarm optimization (TLPSO) algorithm devised in this study and variable neighbourhood search (VNS). Differing from the joint scheduling strategies used in existing research, this study divides the integrated problem into three sub-problems and solves them through three interrelated PSOs named TLPSO. Then, the solutions obtained by TLPSO are enhanced by VNS. The key characteristics of TLPSO and VNS are employed simultaneously to achieve superior solutions in solving the addressed optimization problem. In the proposed hybrid algorithm, the TLPSO performs a global search whereas the VNS has a local search role. These two techniques complement each other to enhance the search diversification and intensification. Numerical experiments on a variety of simulated scenarios show the efficiency and effectiveness of the proposed algorithm by comparing it with other algorithms.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSwarm and evolutionary computation, Nov. 2019, v. 50, 100572en_US
dcterms.isPartOfSwarm and evolutionary computationen_US
dcterms.issued2019-11-
dc.identifier.scopus2-s2.0-85072522341-
dc.identifier.eissn2210-6510en_US
dc.identifier.artn100572en_US
dc.description.validate202402 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0399-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS23026358-
dc.description.oaCategoryGreen (AAM)en_US
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