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

被引：0

作者：

Pitiot, P. ^{[1
,2
]}

Aldanondo, M. ^{[1
]}

Vareilles, E. ^{[1
]}

Gaborit, P. ^{[1
]}

机构：

[1] Univ Toulouse, IMT Mines Albi CGI, Albi, France

[2] Inst Ingn Informat Limoges, Rodez, France

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM) | 2019年

关键词：

product configuration; process configuration; configuration optimization; evolutionary algorithms; clustering; PRODUCT CONFIGURATION;

D O I：

10.1109/ieem44572.2019.8978693

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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 (CFBEA) [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.

引用

页码：795 / 799

页数：5

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