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

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.

Keywords:

• process plan
• job scheduling
• game theory
• hybrid adaptive genetic algorithm
• networked manufacturing.

Acknowledgements

Gratitude is extended to the National Natural Science Foundation of China (Grant No.: 50605050) and Ministry of Education for New Century Excellent Talent Support Program of 2007 (NCET-07-0681) for the financial supports. Special thanks also to Mr. Xuefeng Tian and Miss. Rui Wang for the programming efforts in implementing and demonstrating the presented game solution.