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Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

dc.contributor.authorSoto R.
dc.contributor.authorCrawford B.
dc.contributor.authorAlmonacid B.
dc.contributor.authorParedes F.
dc.date.accessioned2020-09-02T22:28:39Z
dc.date.available2020-09-02T22:28:39Z
dc.date.issued2016
dc.identifier10.1155/2016/9402503
dc.identifier.citation2016, , -
dc.identifier.issn10589244
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6288
dc.descriptionThe Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced. © 2016 Ricardo Soto et al.
dc.language.isoen
dc.publisherHindawi Limited
dc.titleEfficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems
dc.typeArticle


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