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Advanced Shop-Floor Scheduling with Genetic Algorithm for Combined Horizon Optimization in Holonic Manufacturing Systems

  • Cristina Morariu
  • Octavian Morariu
  • Theodor Borangiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8062)

Abstract

Shop-floor scheduling is one of the most complex problems in holonic manufacturing systems as it deals with unpredictable scenarios and overlapping requirements. The scheduling problem is considered to be a NP-hard problem and only near optimal solutions can be obtained. A solution for this problem can be formulated only by employing meta-heuristic class algorithm. This paper discusses the scheduling problem in heterachical operating mode, focusing on solving the local horizon problem. A distributed genetic algorithm is introduced, that uses the local operation plan to generate the initial solution population. The initial population is then evolved based on global optimum soft conditions until the acceptable solution fitness is achieved. The soft conditions considered at global horizon layer are energy footprint, resource utilization and supply chain optimization for resource stocks. The paper describes the data structures used to model this logic and describes in detail the genetic algorithm evolution mechanism. Experimental results are discussed in the context of a pilot production line consisting of six universal resources and a conveyor belt processing two parallel customer orders.

Keywords

Shop-floor scheduling genetic algorithm heterachical operation local horizon intelligent product holonic manufacturing systems 

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References

  1. 1.
    Borangiu, T., Gilbert, P., Ivanescu, N., Rosu, A.: An Implementing framework for holonic manufacturing control with multiple robot-vision stations. Journal of Engineering Applications of Artificial Intelligence (2009) ISSN 0952-1976Google Scholar
  2. 2.
    Borangiu, T., Raileanu, S., Anton, F., Parlea, M., Tahon, C., Berger, T., Trentesaux, D.: Product-driven automation in a service oriented manufacturing cell. In: Proceedings of the Int. Conf. on Industrial Engineering and Systems Management, IESM 2011, Metz, May 25-27 (2011) ISBN 978-2-9600532-3-4Google Scholar
  3. 3.
    McFarlane, D., Sarma, S., Chirn, J.L., Wong, C.Y., Ashton, K.: The intelligent product in manufacturing control and management. In: 15th Triennial World Congress, Barcelona, Spain (July 2002)Google Scholar
  4. 4.
    Meyer, G.G., Främling, K., Holmström, J.: Intelligent products: A survey. Computers in Industry 60(3), 137–148 (2009)CrossRefGoogle Scholar
  5. 5.
    Shen, W., Wang, L., Hao, Q.: 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 (2006)CrossRefGoogle Scholar
  6. 6.
    Wong, C.Y., McFarlane, D., Ahmad Zaharudin, A., Agarwal, V.: The intelligent product driven supply chain. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, p. 6. IEEE (October 2002)Google Scholar
  7. 7.
    Blazewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: Conventional and new solution techniques. European Journal of Operational Research 93, 1–30 (1996)CrossRefGoogle Scholar
  8. 8.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1), 29–41 (1996)CrossRefGoogle Scholar
  9. 9.
    Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Management Science 42(6), 797–813 (1996)CrossRefGoogle Scholar
  10. 10.
    Chong, C.S., et al.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the Winter Simulation Conference, WSC 2006. IEEE (2006)Google Scholar
  11. 11.
    Biegel, J., Davern, J.: Genetic Algorithms and Job Shop Scheduling. Computers and Industrial Engineering 19(1), 81–91 (1990)CrossRefGoogle Scholar
  12. 12.
    Davis, L.: Job Shop Scheduling with Genetic Algorithms. In: International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, pp. 136–140 (1985)Google Scholar
  13. 13.
    Falkenauer, E., Bouffouix, S.: A Genetic Algorithm for Job Shop. In: IEEE International Conferernce on Robotics and Automation, Sacramento, CA, April 9-11, pp. 824–829 (1991)Google Scholar
  14. 14.
    Kanet, J., Sridharan, V.: PROGENITOR: A Genetic Algorithm for Production Scheduling. Wirtschaftsinformatik 33(4), 332–336 (1991)Google Scholar
  15. 15.
    Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry 37(3), 255–274 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Cristina Morariu
    • 1
  • Octavian Morariu
    • 1
  • Theodor Borangiu
    • 1
  1. 1.Dept. of Automation and Applied InformaticsUniversity Politehnica of BucharestBucharestRomania

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