Abstract
We propose a hybrid approach for solving hybrid-flow-shop problems based on the combination of genetic algorithms and a modified Giffler & Thompson (G&T) algorithm. Several extensions of the hybrid-flow-shop are considered and discussed in the context of a real-world example. The genome in the GA encodes a choice of rules to be used to generate production schedules via the G&T algorithm. All constraints to the scheduling task are observed by the G&T algorithm. Therefore, it provides a well suited representation for the GA and leads to a decoupling of domain specific details and genetic optimization. The proposed method is applied to the optimization of a batch annealing plant.
Supported by the BMBF under Grant No. 01IB802A9 (LEONET).
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Kreutz, M., Hanke, D., Gehlen, S. (2000). Solving Extended Hybrid-Flow-Shop Problems Using Active Schedule Generation and Genetic Algorithms. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_29
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DOI: https://doi.org/10.1007/3-540-45356-3_29
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