Advertisement

Intelligent Optimisation for Integrated Process Planning and Scheduling

  • Weidong Li
  • Lihui Wang
  • Xinyu Li
  • Liang Gao
Chapter

Abstract

Traditionally, process planning and scheduling were performed sequentially, where scheduling was executed after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this chapter, a multi-agent-based framework has been developed to facilitate the integration of the two functions. In the framework, the two functions are carried out simultaneously, and an optimisation agent based on evolutionary algorithms is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, experimental studies conducted to compare this approach and some previous works are presented. The experimental results show the proposed approach has achieved significant improvement.

Keywords

Particle Swarm Optimisation Particle Swarm Optimisation Algorithm Shop Floor Manufacturing Resource Schedule Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research work has been supported by collaborative grants from Coventry University, University of Skövde, the State Key Laboratory of Digital Manufacturing Equipment and Technology of the Huazhong University of Science and Technology China, and the Natural Science Foundation of China (NSFC) under Grant no. 51005088.

References

  1. 1.
    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 (pp. 250–254).Google Scholar
  2. 2.
    Kumar, M., & Rajotia, S. (2003). Integration of scheduling with computer aided process planning. Journal of Materials Processing Technology, 138, 297–300.CrossRefGoogle Scholar
  3. 3.
    Saygin, C., & Kilic, S. E. (1999). Integrating flexible process plans with scheduling in flexible manufacturing systems. International Journal of Advanced Manufacturing Technology, 15, 268–280.CrossRefGoogle Scholar
  4. 4.
    Usher, J. M., & Fernandes, K. J. (1996). Dynamic process planning—the static phase. Journal of Materials Processing Technology, 61, 53–58.CrossRefGoogle Scholar
  5. 5.
    Lee, H., & Kim, S. S. (2001). Integration of process planning and scheduling using simulation based genetic algorithms. International Journal of Advanced Manufacturing Technology, 18, 586–590.CrossRefGoogle Scholar
  6. 6.
    Tan, W., & Khoshnevis, B. (2000). Integration of process planning and scheduling—a review. Journal of Intelligent Manufacturing, 11, 51–63.CrossRefGoogle Scholar
  7. 7.
    Chryssolouris, G., & Chan, S. (1985). An integrated approach to process planning and scheduling. Annals of the CIRP, 34(1), 413–417.CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Khoshnevis, B., & Chen, Q. M. (1989). Integration of process planning and scheduling function. In Proceedings of IIE integrated systems conference and society for integrated manufacturing conference (pp. 415–420).Google Scholar
  10. 10.
    Larsen, N. E. (1993). Methods for integration of process planning and production planning. International Journal of Computer Integrated Manufacturing, 6(1–2), 152–162.CrossRefGoogle Scholar
  11. 11.
    Zhang, Y. F., Saravanan, A. N., & Fuh, J. Y. H. (2003). Integration of process planning and scheduling by exploring the flexibility of process planning. International Journal of Production Research, 41(3), 611–628.MATHCrossRefGoogle Scholar
  12. 12.
    Tonshoff, H. K., Beckendorff, U., & Andres, N. (1989). FLEXPLAN: A concept for intelligent process planning and scheduling. In Proceedings of the CIRP international workshop (pp. 319–322).Google Scholar
  13. 13.
    Sormaz, D., & Khoshnevis, B. (2003). Generation of alternative process plans in integrated manufacturing systems. Journal of Intelligent Manufacturing, 14, 509–526.CrossRefGoogle Scholar
  14. 14.
    Kim, Y. K., Park, K., & Ko, J. (2003). A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Computers & Operations Research, 30, 1151–1171.MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Yan, H. S., Xia, Q. F., Zhu, M. R., Liu, X. L., & Guo, Z. M. (2003). Integrated production planning and scheduling on automobile assembly lines. IIE Transactions, 35, 711–725.CrossRefGoogle Scholar
  16. 16.
    Zhang, X. D., & Yan, H. S. (2005). Integrated optimization of production planning and scheduling for a kind of job-shop. International Journal of Advanced Manufacturing Technology, 26, 876–886.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zhang, H. C. (1993). IPPM—a prototype to integrated process planning and job shop scheduling functions. Annals of the CIRP, 42(1), 513–517.CrossRefGoogle Scholar
  18. 18.
    Zhang, W. J., & Xie, S. Q. (2007). Agent technology for collaborative process planning: A review. International Journal of Advanced Manufacturing Technology, 32, 315–325.CrossRefGoogle Scholar
  19. 19.
    Wang, L., Shen, W., & Hao, Q. (2006). An overview of distributed process planning and its integration with scheduling. International Journal of Computer Applications in Technology, 26(1–2), 3–14.CrossRefGoogle Scholar
  20. 20.
    Shen, W., Wang, L., & Hao, Q. (2006). Agent-based distributed manufacturing process planning and scheduling: A state-of-the-art survey. IEEE Transactions on Systems, Man and Cybernetics—Part C: Applications and Reviews, 36(4), 563–577.CrossRefGoogle Scholar
  21. 21.
    Gu, P., Balasubramanian, S., & Norrie, D. (1997). Bidding-based process planning and scheduling in a multi-agent system. Computers & Industrial Engineering, 32(2), 477–496.CrossRefGoogle Scholar
  22. 22.
    Chan, F. T. S., Zhang, J., & Li, P. (2001). Modelling of integrated, distributed and cooperative process planning system using an agent-based approach. Proceedings of Institution of Mechanical Engineering, Part B: Journal of Engineering Manufacturing, 215, 1437–1451.CrossRefGoogle Scholar
  23. 23.
    Wu, S. H., Fuh, J. Y. H., & Nee, A. Y. C. (2002). Concurrent process planning and scheduling in distributed virtual manufacturing. IIE Transactions, 34, 77–89.Google Scholar
  24. 24.
    Lim, M. K., & Zhang, Z. (2003). A multi-agent-based manufacturing control strategy for responsive manufacturing. Journal of Materials Processing Technology, 139, 379–384.CrossRefGoogle Scholar
  25. 25.
    Wang, L., & Shen, W. (2003). DPP: An agent-based approach for distributed process planning. Journal of Intelligent Manufacturing, 14, 429–439.CrossRefGoogle Scholar
  26. 26.
    Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006). Integrated process planning and scheduling/rescheduling—an agent-based approach. International Journal of Production Research, 44(18–19), 3627–3655.MATHCrossRefGoogle Scholar
  27. 27.
    Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006). Dynamic shopfloor scheduling in multi-agent manufacturing system. Expert Systems with Applications, 31, 486–494.CrossRefGoogle Scholar
  28. 28.
    Shukla, S. K., Tiwari, M. K., & Son, Y. J. (2008). Bidding-based multi-agent system for integrated process planning and scheduling: A data-mining and hybrid Tabu-SA algorithm-oriented approach. International Journal of Advanced Manufacturing Technology, 38, 163–175.CrossRefGoogle Scholar
  29. 29.
    Fuji, N., Inoue, R., & Ueda, K. (2008). Integration of process planning and scheduling using multi-agent learning. In Proceedings of 41st CIRP conference on manufacturing systems (pp. 297–300).Google Scholar
  30. 30.
    Nejad, H. T. N., Sugimura, N., Iwamura, K., & Tanimizu, Y. (2008). Agent-based dynamic process planning and scheduling in flexible manufacturing system. In Proceedings of 41st CIRP conference on manufacturing systems (pp. 269–274).Google Scholar
  31. 31.
    Bhaskara Reddy, S. V., Shunmugam, M. S., & Narendran, T. T. (1999). Operation sequencing in CAPP using genetic algorithms. International Journal of Production Research, 37(5), 1063–1074.MATHCrossRefGoogle Scholar
  32. 32.
    Qiao, L., Wang, X. Y., & Wang, S. C. (2000). A GA-based approach to machining operation sequencing for prismatic parts. International Journal of Production Research, 38(14), 3283–3303.MATHCrossRefGoogle Scholar
  33. 33.
    Yip-Hoi, D., & Dutta, D. (1996). A genetic algorithm application for sequencing operations in process planning for parallel machining. IIE Transactions, 28, 55–68.CrossRefGoogle Scholar
  34. 34.
    Zhang, F., Zhang, Y. F., & Nee, A. Y. C. (1997). Using genetic algorithms in process planning for job shop machining. IEEE Transactions on Evolutional Computation, 1, 278–289.CrossRefGoogle Scholar
  35. 35.
    Ding, L., Yue, Y., Ahmet, K., Jackson, M., & Parkin, R. (2005). Global optimization of a feature-based process sequence using GA and ANN techniques. International Journal of Production Research, 43(15), 3247–3272.MATHCrossRefGoogle Scholar
  36. 36.
    Morad, N., & Zalzala, A. (1999). Genetic algorithms in integrated process planning and scheduling. Journal of Intelligent Manufacturing, 10, 169–179.CrossRefGoogle Scholar
  37. 37.
    Ma, G. H., Zhang, Y. F., & Nee, A. Y. C. (2000). A simulated annealing-based optimization for process planning. International Journal of Production Research, 38(12), 2671–2687.CrossRefGoogle Scholar
  38. 38.
    Lee, D. H., Kiritsis, D., & Xirouchakis, P. (2001). Search heuristics for operation sequencing in process planning. International Journal of Production Research, 39, 3771–3788.MATHCrossRefGoogle Scholar
  39. 39.
    Li, W. D., & McMahon, C. A. (2007). A simulated annealing-based optimization approach for integrated process planning and scheduling. International Journal of Computer Integrated Manufacturing, 20(1), 80–95.CrossRefGoogle Scholar
  40. 40.
    Li, W. D., Ong, S. K., & Nee, A. Y. C. (2004). Optimization of process plans using a constraint-based tabu search approach. International Journal of Production Research, 42(10), 1955–1985.MATHCrossRefGoogle Scholar
  41. 41.
    Li, W. D., Gao, L., Li, X. Y., & Guo, Y. (2008). Game theory-based cooperation of process planning and scheduling. In Proceedings of CSCWD (pp. 841–845).Google Scholar
  42. 42.
    Guo, Y. W., Mileham, A. R., Owen, G. W., & Li, W. D. (2006). Operation sequencing optimization using a particle swarm optimization approach. Proceedings of the Institution of Mechanical Engineers, Journal of Engineering Manufacture, Part B, 220(B12), 1945–1958.CrossRefGoogle Scholar
  43. 43.
    Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. IV, pp. 1942–1948).Google Scholar
  44. 44.
    Li, W. D., Ong, S. K., & Nee, A. Y. C. (2002). Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. International Journal of Production Research, 40(8), 1899–1922.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Faculty of Engineering and ComputingCoventry UniversityCoventryUK
  2. 2.Virtual Systems Research CentreUniversity of SkövdeSkövdeSweden
  3. 3.State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations