Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207543.2011.571459
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dc.titleA hybrid particle swarm based method for process planning optimisation
dc.contributor.authorWang, Y.F.
dc.contributor.authorZhang, Y.F.
dc.contributor.authorFuh, J.Y.H.
dc.date.accessioned2014-06-16T09:29:26Z
dc.date.available2014-06-16T09:29:26Z
dc.date.issued2012-01-01
dc.identifier.citationWang, Y.F., Zhang, Y.F., Fuh, J.Y.H. (2012-01-01). A hybrid particle swarm based method for process planning optimisation. International Journal of Production Research 50 (1) : 277-292. ScholarBank@NUS Repository. https://doi.org/10.1080/00207543.2011.571459
dc.identifier.issn00207543
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54278
dc.description.abstractA process planning (PP) problem is defined as to determine a set of operation-methods (machine, tool, and set-up configuration) that can convert the given stock to the designed part. Essentially, the PP problem involves the simultaneous decision making of two tasks: operation-method selection and sequencing. This is a combinatorial optimisation problem and it is difficult to find the best solution in a reasonable amount of time. In this article, an optimisation approach based on particle swarm optimisation (PSO) is proposed to solve the PP problem. Due to the characteristic of discrete process planning solution space and the continuous nature of the original PSO, a novel solution representation scheme is introduced for the application of PSO in solving the PP problem. Moreover, two kinds of local search algorithms are incorporated and interweaved with PSO evolution to improve the best solution in each generation. The numerical experiments and analysis have demonstrated that the proposed algorithm is capable of gaining a good quality solution in an efficient way. © 2012 Copyright Taylor and Francis Group, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1080/00207543.2011.571459
dc.sourceScopus
dc.subjectcombinatorial optimisation
dc.subjectcomputer-aided process planning
dc.subjectlocal search
dc.subjectoperation selection
dc.subjectoperation sequencing
dc.subjectparticle swarm optimisation
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1080/00207543.2011.571459
dc.description.sourcetitleInternational Journal of Production Research
dc.description.volume50
dc.description.issue1
dc.description.page277-292
dc.description.codenIJPRB
dc.identifier.isiut000301951700016
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