A game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturing

Authors:
Guanghui Zhou;Zhongdong Xiao;Pingyu Jiang;George Q. Huang
Affiliations:
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China;School of Management, Xi'an Jiaotong University, Xi'an, China,The Key Lab of the Ministry of Education for Process Control & Efficiency Engineering, Xi'an Jiaotong University, Xi'an, China;State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China,School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, China
Venue:
International Journal of Computer Integrated Manufacturing
Year:
2010

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Near optimal process plan selection for multiple jobs in networked based manufacturing using multi-objective evolutionary algorithms

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Abstract

This study seeks to address an approach for generating optimal process plans for multiple jobs in networked manufacturing. Because of production flexibility, generating several feasible process plans for each job is possible. Concerning the networked manufacturing mode, the specific scenario of competitive relationships, like delivery time existing between different jobs, should be taken into account in generating the optimal process plan for each job. As such, in this study, an N-person non-cooperative game-theoretic mathematical solution with complete information is proposed to generate the optimal process plans for multiple jobs. The game is divided into two kinds of sub-games, i.e. process plan decision sub-game and job scheduling sub-game. The former sub-game provides the latter ones with players while the latter ones decide payoff values for the former one to collaboratively arrive at the Nash equilibrium (NE). Endeavouring to solve this game more efficiently and effectively, a two-level nested solution algorithm using a hybrid adaptive genetic algorithm (HAGA) is developed. Finally, numerical examples are carried out to investigate the feasibility of the approach proposed in the study.