Particle swarm optimisation for open shop problems with fuzzy durations
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In this paper we confront a variation of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. We propose a particle swarm optimization (PSO) approach to minimise the expected makespan using priorities to represent particle position, as well as a decoding algorithm to generate schedules in a subset of possibly active ones. Finally, the performance of the PSO is tested on several benchmark problems, modified so as to have fuzzy durations, compared with a memetic algorithm from the literature
In this paper we confront a variation of the open shop problem where task durations are allowed to be uncertain and where uncertainty is modelled using fuzzy numbers. We propose a particle swarm optimization (PSO) approach to minimise the expected makespan using priorities to represent particle position, as well as a decoding algorithm to generate schedules in a subset of possibly active ones. Finally, the performance of the PSO is tested on several benchmark problems, modified so as to have fuzzy durations, compared with a memetic algorithm from the literature
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4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011. Proceedings, Part I
La publicación final está disponible en Springer http://dx.doi.org/10.1007/978-3-642-21344-1_38
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20110659
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This work is supported by the Spanish Ministry of Science and Education under research grant MEC-FEDER TIN2010-20976-C02-02
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