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Multi-Objective Genetic Algorithm for Machine Selection in Dynamic Process Planning  

Choi, Hoe-Ryeon (한국과학기술연구원 지능인터랙션연구센터)
Kim, Jae-Kwan (한국과학기술연구원 지능인터랙션연구센터)
Lee, Hong-Chul (고려대학교 정보경영공학부)
Rho, Hyung-Min (한국과학기술연구원 미래융합기술연구소)
Publication Information
Journal of the Korean Society for Precision Engineering / v.24, no.4, 2007 , pp. 84-92 More about this Journal
Abstract
Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.
Keywords
Dynamic process planning; Machine loads; Multi-Objective genetic algorithm; K-means algorithm;
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