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Evolutionary algorithms for production planning problems with setup decisions
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Evolutionary algorithms for production planning problems with setup decisions

Y.-F. Hung, C.-C. ShihC.-P. Chen
Journal of the Operational Research Society, 卷.50(8), 頁碼.857-866
1999

摘要

Aggregate production planning Capacitated lot sizing Genetic algorithm Mixed integer programming Production planning with setups Management Information Systems Strategy and Management Management Science and Operations Research Marketing
Production planning problems with setup decisions, which were formulated as mixed integer programmes (MIP), are solved in this study. The integer component of the MIP solution is determined by three evolution algorithms used in this study. Firstly, a traditional genetic algorithm (GA) uses conventional crossover andmutation operators for generating new chromosomes (solutions). Secondly, a modified GA uses not only the conventional operators but also a sibling operator, which stochastically produces new chromosomes from old ones using the sensitivity information of an associated linear programme. Thirdly, a sibling evolution algorithm uses only the sibling operator to reproduce. Based on the experiments done in this study, the sibling evolution algorithm performs the best among all the algorithmsused in this study. © 1999 Operational Research Society Ltd. All rights reserved.

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