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.
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References
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)
Applegate, D., Cook, W.: A Computational Study of the Job Shop Scheduling problem. ORSA Journal on Computing 3(2), 149–156 (1991)
Barker, J.R., McMahon, G.B.: Scheduling the General Job-Shop. Management Science 31(5), 594–598 (1985)
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)
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)
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)
Braslaw, J.: Personal communication, Material Sciences Department, Ford Research (2001)
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)
Cicirello, V.A., Smith, S.F.: Wasp-like Agents for Distributed Factory Coordination. The Robotics Institute CMU (2001)
Conway, R.W., Maxwell, W.L., Miller, L.W.: Theory of Scheduling, pp. 6–8. Addison Wesley (1995)
FIPA, Foundation for Intelligent Physical Agents: FIPA Modeling Area: Temporal Constraints (2003)
FIPA, Foundation for Intelligent Physical Agents: FIPA Modeling Area: Interaction Diagrams (2003)
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)
Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research, 8th edn., pp. 440–466. McGraw Hill (2006)
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)
Manne, A.S.: On the Job-Shop Scheduling Problem. Operations Research 8, 219–223 (1960)
Hammadi, M.K., Slim, B.P.: Evolutionary Algorithms For Job-Shop Scheduling. Int. J. Appl. Math. Comput. Sci. 14(1), 91–103 (2004)
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)
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)
Nabil, N., Elsayed, E.A.: Job shop scheduling with alternative machines. International Journal of Production Research 28(9), 1595–1609 (1990)
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)
Odell, J., Parunak, V.D., Bauer, B.: Extending UML for Agents. In: AOIS Workshop en AAAI 2000 (2000)
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)
Pinedo, M.: Scheduling: Theory, Algorithms and Systems, 3rd edn., p. 1. Springer (2008)
Stancovic, A.: Misconceptions about Real Time Computing. IEE Computer, 10–19 (1988)
Taillard, E.: Parallel Taboo Search Techniques for the Job Shop Scheduling Problem. ORSA Journal on Computing 6, 108–117 (1994)
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)
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)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms: analizing the state-of-the-art. Evolutionary Computation 8(2), 125–147 (2000)
Takeshi, Y., Ryohei, N.: Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search, pp. 237–248. Kluwer academic Publishers, MA (1996)
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)
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Gutierrez-Marfileno, L.F., Ponce-de-Leon, E., Diaz-Diaz, E., Ibarra-Martinez, L. (2013). Wasp Colony with a Multiobjective Local Optimizer for Dynamic Task Planning in a Production Plant. In: Schütze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31519-0_29
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DOI: https://doi.org/10.1007/978-3-642-31519-0_29
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