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Application of Game Theory-Based Hybrid Algorithm for Multi-objective IPPS

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Effective Methods for Integrated Process Planning and Scheduling

Part of the book series: Engineering Applications of Computational Methods ((EACM,volume 2))

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Abstract

The research in this chapter focused on the multi-objective Integrated Process Planning and Scheduling (IPPS) problem. In this research, a game theory-based approach has been used to deal with multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method for the research of the multi-objective IPPS problem.

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References

  1. Baykasoglu A, Ozbakir L (2009) A grammatical optimization approach for integrated process planning and scheduling. J Intell Manuf 20:211–221

    Article  Google Scholar 

  2. Beckendorff U, Kreutzfeldt J, Ullmann W (1991) Reactive workshop scheduling based on alternative routings. In: Proceedings of a conference on factory automation and information management, pp 875–885

    Google Scholar 

  3. Chan FTS, Kumar V, Tiwari MK (2009) The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling model. Int J Prod Res 47(1):119–142

    Article  Google Scholar 

  4. Chryssolouris G, Chan S, Cobb W (1984) Decision making on the factory floor: an integrated approach to process planning and scheduling. Robot Comput-Integr Manuf 1(3–4):315–319

    Article  Google Scholar 

  5. Chryssolouris G, Chan S (1985) An integrated approach to process planning and scheduling. Ann CIRP 34(1):413–417

    Article  Google Scholar 

  6. Guo YW, Li WD, Mileham AR, Owen GW (2009) Optimisation of integrated process planning and scheduling using a particle swarm optimization approach. Int J Prod Res 47(14):3775–3796

    Article  Google Scholar 

  7. Guo YW, Li WD, Mileham AR, Owen GW (2009) Applications of particle swarm optimisation in integrated process planning and scheduling. Robot Comput-Integr Manuf 25(2):280–288

    Article  Google Scholar 

  8. Hsu T, Dupas R, Jolly D, Goncalves G (2002) Evaluation of mutation heuristics for the solving of multiobjective flexible job shop by an evolutionary algorithm. In: Proceedings of the 2002 IEEE international conference on systems, man and cybernetics, vol 5, pp 655–660

    Google Scholar 

  9. Khoshnevis B, Chen QM (1989) Integration of process planning and scheduling function. In: Proceedings of IIE integrated systems conference & society for integrated manufacturing conference, pp 415–420

    Google Scholar 

  10. Kim KH, Song JY, Wang KH (1997) A negotiation based scheduling for items with flexible process plans. Comput Ind Eng 33(3–4):785–788

    Google Scholar 

  11. Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151–1171

    Article  MathSciNet  Google Scholar 

  12. Kuhnle H, Braun HJ, Buhring J (1994) Integration of CAPP and PPC—interfusion manufacturing management. Integr Manuf Syst 5(2):21–27

    Article  Google Scholar 

  13. Kumar M, Rajotia S (2003) Integration of scheduling with computer aided process planning. J Mater Process Technol 138:297–300

    Article  Google Scholar 

  14. Larsen NE (1993) Methods for integration of process planning and production planning. Int J Comput Integr Manuf 6(1–2):152–162

    Article  Google Scholar 

  15. Lee H, Kim SS (2001) Integration of process planning and scheduling using simulation based genetic algorithms. Int J Adv Manuf Technol 18:586–590

    Article  Google Scholar 

  16. Li WD, McMahon CA (2007) A simulated annealing—based optimization approach for integrated process planning and scheduling. Int J Comput Integr Manuf 20(1):80–95

    Article  Google Scholar 

  17. Li WD, Gao L, Li XY, Guo Y (2008) Game theory-based cooperation of process planning and scheduling. In: Proceeding of the 12th international conference on computer supported cooperative work in design, China, pp 841–845

    Google Scholar 

  18. Li XY, Gao L, Zhang CY, Shao XY (2010) A review on integrated process planning and scheduling. Int J Manuf Res 5(2):161–180

    Article  Google Scholar 

  19. Li XY, Gao L, Shao XY, Zhang CY, Wang CY (2010) Mathematical modeling and evolutionary algorithm based approach for integrated process planning and scheduling. Comput Oper Res 37:656–667

    Article  Google Scholar 

  20. Li XY, Zhang CY, Gao L, Li WD, Shao XY (2010) An agent-based approach for integrated process planning and scheduling. Expert Syst Appl 37:1256–1264

    Article  Google Scholar 

  21. Li XY, Shao XY, Gao L, Qian WR (2010) An effective hybrid algorithm for integrated process planning and scheduling. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2010.04.001

    Article  Google Scholar 

  22. Morad N, Zalzala AMS (1999) Genetic algorithms in integrated process planning and scheduling. J Intell Manuf 10:169–179

    Article  Google Scholar 

  23. Shao XY, Li XY, Gao L, Zhang CY (2009) Integration of process planning and scheduling—a modified genetic algorithm-based approach. Comput Oper Res 36:2082–2096

    Article  Google Scholar 

  24. Shen WM, Wang LH, Hao Q (2006) Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. IEEE Trans Syst, Man, Cybern-Part C: Appl Rev 36(4):563–577

    Article  Google Scholar 

  25. Shukla SK, Tiwari MK, Son YJ (2008) Bidding-based multi-agent system for integrated process planning and scheduling: a data-mining and hybrid tabu-sa algorithm-oriented approach. Int J Adv Manuf Technol 38:163–175

    Article  Google Scholar 

  26. Sugimura N, Hino R, Moriwaki T (2001) Integrated process planning and scheduling in holonic manufacturing systems. In: Proceedings of IEEE international symposium on assembly and task planning soft research park, vol 4, pp 250–254

    Google Scholar 

  27. Tan W, Khoshnevis B (2000) Integration of process planning and scheduling—a review. J Intell Manuf 11:51–63

    Article  Google Scholar 

  28. Thomalla CS (2001) Job shop scheduling with alternative process plans. Int J Prod Econ 74:125–134

    Article  Google Scholar 

  29. Usher JM, Fernandes KJ (1996) Dynamic process planning-the static phase. J Mater Process Technol 61:53–58

    Google Scholar 

  30. Wang LH, Song YJ, Shen WM (2005) Development of a function block designer for collaborative process planning. In: Proceeding of CSCWD2005. Coventry, UK, pp 24-26

    Google Scholar 

  31. Wang LH, Shen WM, Hao Q (2006) An overview of distributed process planning and its integration with scheduling. Int J Comput Appl Technol 26(1–2):3–14

    Article  Google Scholar 

  32. Wong TN, Leung CW, Mak KL, Fung RYK (2006) Integrated process planning and scheduling/ rescheduling—an agent-based approach. Int J Prod Res 44(18–19):3627–3655

    Article  Google Scholar 

  33. Zhang HC (1993) IPPM-a prototype to integrated process planning and job shop scheduling functions. Ann CIRP 42(1):513–517

    Article  Google Scholar 

  34. Zhang J, Gao L, Chan FTS (2003) A holonic architecture of the concurrent integrated process planning system. J Mater Process Technol 139:267–272

    Article  Google Scholar 

  35. Zhang WQ, Gen M (2010) Process planning and scheduling in distributed manufacturing system using multiobjective genetic algorithm. IEEJ Trans Electr Electron Eng 5:62–72

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Xinyu Li

  2. School of Mechanical Science and Engineering, HUST, State Key Laboratory of Digital Manufacturing and Equipment Technology, Wuhan, Hubei, China

    Liang Gao

Authors
  1. Xinyu Li
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  2. Liang Gao
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Correspondence to Xinyu Li .

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Li, X., Gao, L. (2020). Application of Game Theory-Based Hybrid Algorithm for Multi-objective IPPS. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_16

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  • DOI: https://doi.org/10.1007/978-3-662-55305-3_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-55303-9

  • Online ISBN: 978-3-662-55305-3

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