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Wasp Colony with a Multiobjective Local Optimizer for Dynamic Task Planning in a Production Plant

  • Luis Fernando Gutierrez-Marfileno
  • Eunice Ponce-de-Leon
  • Elva Diaz-Diaz
  • Leoncio Ibarra-Martinez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

Dynamic task scheduling is a time-dependent optimization issue. In this work, we modeled the process that is performed at a production plant as a task scheduling issue, in which a production line sends trucks to a painting plant with several stations. The objective is to attain efficient task scheduling, taking into account three conflicting objectives: number of color changes in booths, work tardiness, and makespan. In order to solve this problem, we developed a hybrid technique, which comprises a Wasp Colony algorithm and a set of priority rules. Both the problem and its solution were modeled through Agent Unified Modeling Languague (AUML) so as to achieve implementation. The results were a remarkable decrease in the number of color changes and work tardiness and the preservation of the number of painted trucks within an acceptable magnitude.

Keywords

Assembly Line Multiagent System Task Schedule Response Threshold Priority Rule 
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.

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References

  1. 1.
    Abbass, H.A.: The self-adaptive Pareto differential evolution algorithm (online). In: Congress on Evolutionary Computation, Piscataway, NJ, USA, vol. 1, pp. 831–836 (2000)Google Scholar
  2. 2.
    Applegate, D., Cook, W.: A Computational Study of the Job Shop Scheduling problem. ORSA Journal on Computing 3(2), 149–156 (1991)zbMATHCrossRefGoogle Scholar
  3. 3.
    Barker, J.R., McMahon, G.B.: Scheduling the General Job-Shop. Management Science 31(5), 594–598 (1985)zbMATHCrossRefGoogle Scholar
  4. 4.
    Binato, S., Hery, W., Loewenstern, D.Y., Resende, M.: A GRASP for Job Scheduling. Technical Report N 00.6.1 AT&T Labs Research (1999–200)Google Scholar
  5. 5.
    Bonabeau, E., Theraulaz, G., Deneubourg, J.L.: Fixed response threshold and the regulation of division of labor in insect societies. Bulletin of Mathematical Biology 60, 753–807 (1998)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bonabeau, E., Sobkowski, A., Theraulaz, G., Deneubourg, J.L.: Adaptive task allocation inspired by a model of division of labor in social insects. In: Lundh, D., Olsson, B. (eds.) Bio Computation and Emergent Computing, pp. 36–45. World Scientific (1997)Google Scholar
  7. 7.
    Braslaw, J.: Personal communication, Material Sciences Department, Ford Research (2001)Google Scholar
  8. 8.
    Cao, Y., Yang, Y., Wang, H., Yang, L.: Intelligent Job Shop Scheduling Based on MAS and Integrated Routing Wasp Algorithm and Scheduling Wasp Algorithm. Journal of Software 4(5) (2009)Google Scholar
  9. 9.
    Cicirello, V.A., Smith, S.F.: Wasp-like Agents for Distributed Factory Coordination. The Robotics Institute CMU (2001)Google Scholar
  10. 10.
    Conway, R.W., Maxwell, W.L., Miller, L.W.: Theory of Scheduling, pp. 6–8. Addison Wesley (1995)Google Scholar
  11. 11.
    FIPA, Foundation for Intelligent Physical Agents: FIPA Modeling Area: Temporal Constraints (2003)Google Scholar
  12. 12.
    FIPA, Foundation for Intelligent Physical Agents: FIPA Modeling Area: Interaction Diagrams (2003)Google Scholar
  13. 13.
    Gutierrez Marfileno Luis, F., de Leon Eunice, P.: Modelado de un sistema en tiempo real mediante AUML, 11o Seminario de Investigacion. Universidad Autonoma de Aguascalientes (2010)Google Scholar
  14. 14.
    Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research, 8th edn., pp. 440–466. McGraw Hill (2006)Google Scholar
  15. 15.
    Omar, M., Baharum, A., Hasan, Y.A.: A job-shop scheduling problem (JSSP) using Genetic Algorithm (GA). In: Proceedings of 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications University Sains Malasya, Penang (June 2006)Google Scholar
  16. 16.
    Manne, A.S.: On the Job-Shop Scheduling Problem. Operations Research 8, 219–223 (1960)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Hammadi, M.K., Slim, B.P.: Evolutionary Algorithms For Job-Shop Scheduling. Int. J. Appl. Math. Comput. Sci. 14(1), 91–103 (2004)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Morley, D.: Painting trucks at general motors: The effectiveness of a complexity-based approach. Embracing Complexity: Exploring the Application of Complex Adaptive Systems to Business, The Ernst and Young Center for Business Innovation, pp. 53–58 (1996)Google Scholar
  19. 19.
    Morley, D., Schelberg, C.: An analysis of a plant-specific dynamic scheduler. In: Proceedings of the NSF Workshop on Dynamic Scheduling, pp. 115–122 (1993)Google Scholar
  20. 20.
    Nabil, N., Elsayed, E.A.: Job shop scheduling with alternative machines. International Journal of Production Research 28(9), 1595–1609 (1990)zbMATHCrossRefGoogle Scholar
  21. 21.
    Nouyan, S.: Agent-Based Approach to Dynamic Task Allocation. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) ANTS 2002. LNCS, vol. 2463, pp. 28–39. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Odell, J., Parunak, V.D., Bauer, B.: Extending UML for Agents. In: AOIS Workshop en AAAI 2000 (2000)Google Scholar
  23. 23.
    Picard, G.: UML Stereotypes Definition and AUML Notations for ADELFE Methodology with OpenTool. In: First European Workshop on Multi-Agent Systems. St. Catherine College Oxford (2003)Google Scholar
  24. 24.
    Pinedo, M.: Scheduling: Theory, Algorithms and Systems, 3rd edn., p. 1. Springer (2008)Google Scholar
  25. 25.
    Stancovic, A.: Misconceptions about Real Time Computing. IEE Computer, 10–19 (1988)Google Scholar
  26. 26.
    Taillard, E.: Parallel Taboo Search Techniques for the Job Shop Scheduling Problem. ORSA Journal on Computing 6, 108–117 (1994)zbMATHCrossRefGoogle Scholar
  27. 27.
    Theraulaz, G., Goss, S., Gervet, J., Deneubourg, J.L.: Task differentiation in polistes wasp colonies. In: Proc. of the First Intl. Conf. on Simulation of Adaptive Behavior. MIT Press (1991)Google Scholar
  28. 28.
    Theraulaz, G., Bonabeau, E., Deneubourg, J.L.: Response threshold reinforcement and division of labour in insect societies. Proc. R. Soc. London B 265(1393), 327–335 (1998)CrossRefGoogle Scholar
  29. 29.
    Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms: analizing the state-of-the-art. Evolutionary Computation 8(2), 125–147 (2000)CrossRefGoogle Scholar
  30. 30.
    Takeshi, Y., Ryohei, N.: Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search, pp. 237–248. Kluwer academic Publishers, MA (1996)Google Scholar
  31. 31.
    Zhou, D.N., Cherkassky, V., Baldwin, T.R., Olson, D.E.: A Neural Network Approach to Job-shop Scheduling. IEEE Trans. Neural Networks 2(1), 175–179 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luis Fernando Gutierrez-Marfileno
    • 1
  • Eunice Ponce-de-Leon
    • 1
  • Elva Diaz-Diaz
    • 1
  • Leoncio Ibarra-Martinez
    • 1
  1. 1.Universidad Autonoma de AguascalientesAguascalientesMexico

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