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Product/Process Configuration Evolutionary Optimization: A Multiobjective Clustering in Order to Reduce Inconsistencies During Crossover

Abstract : Concurrent configuration of a product and its associated production process is a challenging problem in customer/supplier relations dealing with configurable products. Search for optimized solutions that respect customer’s needs and constraints of the problem in a multiobjective context is a particularly difficult task. Constraints Filtering Based Evolutionary Algorithm (CFB- EA) [1] proposes an original way to integrate constraints satisfaction in optimization thanks to a constraints filtering engine. CFB-EA tries to mix solutions randomly selected in order to improve them but leads to many incompatibility occurrences which are time consuming. We propose in this article a dedicated multiobjective clustering algorithm that reduces incompatibilities occurrences and improve the selection of solutions for crossover operator.
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Submitted on : Friday, February 7, 2020 - 4:26:40 PM
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Paul Pitiot, Michel Aldanondo, Élise Vareilles, Paul Gaborit. Product/Process Configuration Evolutionary Optimization: A Multiobjective Clustering in Order to Reduce Inconsistencies During Crossover. IEEM 2019 - IEEE International Conference on Industrial Engineering and Engineering Management, Dec 2019, Macao, China. 5 p., ⟨10.1109/IEEM44572.2019.8978693⟩. ⟨hal-02444050⟩

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