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

TitleA game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturing
Authors
KeywordsGame Theory
Hybrid Adaptive Genetic Algorithm
Job Scheduling
Networked Manufacturing
Process Plan
Issue Date2010
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.asp
Citation
International Journal Of Computer Integrated Manufacturing, 2010, v. 23 n. 12, p. 1118-1132 How to Cite?
AbstractThis 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. © 2010 Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/155937
ISSN
2015 Impact Factor: 1.319
2015 SCImago Journal Rankings: 0.673
ISI Accession Number ID
Funding AgencyGrant Number
National Natural Science Foundation of China50605050
Ministry of EducationNCET-07-0681
Funding Information:

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.

References

 

DC FieldValueLanguage
dc.contributor.authorZhou, Gen_US
dc.contributor.authorXiao, Zen_US
dc.contributor.authorJiang, Pen_US
dc.contributor.authorHuang, GQen_US
dc.date.accessioned2012-08-08T08:38:30Z-
dc.date.available2012-08-08T08:38:30Z-
dc.date.issued2010en_US
dc.identifier.citationInternational Journal Of Computer Integrated Manufacturing, 2010, v. 23 n. 12, p. 1118-1132en_US
dc.identifier.issn0951-192Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/155937-
dc.description.abstractThis 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. © 2010 Taylor & Francis.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.aspen_US
dc.relation.ispartofInternational Journal of Computer Integrated Manufacturingen_US
dc.subjectGame Theoryen_US
dc.subjectHybrid Adaptive Genetic Algorithmen_US
dc.subjectJob Schedulingen_US
dc.subjectNetworked Manufacturingen_US
dc.subjectProcess Planen_US
dc.titleA game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturingen_US
dc.typeArticleen_US
dc.identifier.emailHuang, GQ:gqhuang@hkucc.hku.hken_US
dc.identifier.authorityHuang, GQ=rp00118en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1080/0951192X.2010.524248en_US
dc.identifier.scopuseid_2-s2.0-78649506002en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78649506002&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume23en_US
dc.identifier.issue12en_US
dc.identifier.spage1118en_US
dc.identifier.epage1132en_US
dc.identifier.isiWOS:000284634500005-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridZhou, G=35188416000en_US
dc.identifier.scopusauthoridXiao, Z=36676735100en_US
dc.identifier.scopusauthoridJiang, P=7201470064en_US
dc.identifier.scopusauthoridHuang, GQ=7403425048en_US
dc.identifier.citeulike8343633-

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