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A Multiple Ant Colony Optimisation Approach for a Multi-objective Manufacturing Rescheduling Problem

  • Vikas Kumar
  • Nishikant Mishra
  • Felix T. S. Chan
  • Niraj Kumar
  • Anoop Verma
Chapter

Abstract

Manufacturing scheduling is a well-known complex optimisation problem. A flexible manufacturing system on one side eases the manufacturing processes but on the other hand it increases the complexity in the decision making processes. This complexity further enhances when disruption in the manufacturing processes occurs or when arrival of new orders is considered. This requires rescheduling of the whole operation, which is a complex decision making process. Realising this complexity and taking into account the contradictory objective of making a trade-off between costs and time, this research aims to generate an effective manufacturing schedule. The existing approach of rescheduling sometimes generates entirely a new plan that requires a lot of changes in the decisions, which is not preferable by manufacturing firms. Therefore, in this research whenever a disruption occurs or a new order arrives, the proposed approach reschedules the remaining manufacturing operations in such a way that minimum changes occur in the original manufacturing plan. Evolutionary optimisation methods have been quite successful and widely addressed by researchers to handle such complex multi-objective optimisation problems because of their ability to find multiple optimal solutions in one single simulation run. Inspired by this, the present research proposes a multiple ant colony optimisation (MACO) algorithm to resolve the computational complexity of a manufacturing rescheduling problem. The performance of the proposed MACO algorithm will be compared with the simple ant colony optimisation (ACO) to judge its robustness and efficacy.

Keywords

Particle Swarm Optimisation Flexible Manufacturing System Vehicle Rout Problem Tabu List Pheromone Trail 
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.

References

  1. 1.
    Yamamoto, M. (1985). Scheduling/rescheduling in the manufacturing operating system environment. International Journal of Production Research, 23(4), 705–722.CrossRefGoogle Scholar
  2. 2.
    Wu, S. D., Storer, R. H., & Chang, P. C. (1993). One-machine rescheduling heuristics with efficiency and stability as criteria. Computers and Operations Research, 20(1), 1–14.MATHCrossRefGoogle Scholar
  3. 3.
    Abumaizar, A. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065–2082.MATHCrossRefGoogle Scholar
  4. 4.
    Jain, A. K., & ElMaraghy, H. A. (1997). Production scheduling/rescheduling in flexible manufacturing. International Journal of Production Research, 35(1), 281–309.MATHCrossRefGoogle Scholar
  5. 5.
    Fang, H.L., Ross, P. & Corne, D. (1993). A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems. In S. Forrest (Ed.), Proceedings of the 1st Annual Conference on Genetic Algorithms (pp. 375–382) San Mateo: Morgan Kaufmann.Google Scholar
  6. 6.
    Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: a framework of strategies, policies, and methods. Journal of Scheduling, 6(1), 39–62.MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Silva, C. A., Sousa, J. M. C., & Runkler, T. A. (2008). Rescheduling and optimization of logistic processes using GA and ACO. Engineering Applications of Artificial Intelligence, 21(3), 343–352.CrossRefGoogle Scholar
  8. 8.
    Hozak, K., & Hill, J. A. (2009). Issues and opportunities regarding replanning and rescheduling frequencies. International Journal of Production Research, 47(18), 4955–4970.MATHCrossRefGoogle Scholar
  9. 9.
    Potthoff, D., Huisman, D. & Desaulniers, G. (2010). Column generation with dynamic duty selection for railway crew rescheduling. Transportation Science, published online in Articles in Advance, May 25, 2010.Google Scholar
  10. 10.
    Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. Vol. 4. (pp. 1942–1948).Google Scholar
  11. 11.
    Dorigo, M. (1992). Optimization, Learning and Natural Algorithms, PhD Thesis, Politecnico di Milano, Italie.Google Scholar
  12. 12.
    Pham, D.T. & Ghanbarzadeh, A. (2007). Multi-objective optimization using the Bees Algorithm. Proceedings of IPROMS 2007 Conference. Google Scholar
  13. 13.
    Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 8(2), 173–195.CrossRefGoogle Scholar
  14. 14.
    Tan, K. C., Goha, C. K., Mamuna, A. A., & Ei, E. Z. (2008). An evolutionary artificial immune system for multi-objective optimization. European Journal of Operational Research, 187(2), 371–392.MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Wei, L., & Yuying, Y. (2008). Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm. Journal of Materials Processing Technology, 208(1–3), 499–506.CrossRefGoogle Scholar
  16. 16.
    Sbalzarini, I.F., Müller, S. & Koumoutsakos, P. (2000). Multiobjective optimization using evolutionary algorithms. Proceedings of the Summer Program, Center for Turbulence Research, NASA.Google Scholar
  17. 17.
    Jozefowiez, N., Semet, F., & Talbi, E. G. (2008). Multi-objective vehicle routing problems. European Journal of Operational Research, 189(2), 293–309.MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Coello, C. A. (2006). Evolutionary multiobjective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28–36.CrossRefGoogle Scholar
  19. 19.
    Schaffer, J.D. (1984). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. PhD Thesis, Vanderbilt University.Google Scholar
  20. 20.
    Hajela, P., & Lin, C. Y. (1992). Genetic search strategies in multi-criterion optimal design. Structural Optimization, 4, 99–107.CrossRefGoogle Scholar
  21. 21.
    Deb, K. & Jain, S. (2002). Running performance metrics for evolutionary multi-objective optimization. Technical Report, KanGAL, Indian Institute of Technology, Kanpur 208016, India.Google Scholar
  22. 22.
    Loetamonphong, J., Fang, S. H., & Young, R. E. (2002). Multi-objective optimization problems with fuzzy relation equation constraints. Fuzzy Sets and Systems, 127(2), 141–164.MathSciNetMATHCrossRefGoogle Scholar
  23. 23.
    Srinivas, N., & Deb, K. (1994). Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. MIT Press, 2(3), 221–248.Google Scholar
  24. 24.
    Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: a tutorial. Reliability Engineering and System Safety, 91(9), 992–1007.CrossRefGoogle Scholar
  25. 25.
    Dorigo, M., Birattari, M. & Stǘtzle, T. (2006). Ant colony optimization: artificial ants as a computational intelligence technique. IRIDIA—Technical Report Series, Technical Report No. TR/IRIDIA/2006-023.Google Scholar
  26. 26.
    Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, Cybernetics—Part B: Cybernetics, 26(1), 29–41.CrossRefGoogle Scholar
  27. 27.
    Gravel, M., Price, W. L., & Gagné, C. (2002). Scheduling continuous casting of aluminium using a multiple objective ant colony optimization metaheuristic. European Journal of Operational Research, 143(1), 218–229.MATHCrossRefGoogle Scholar
  28. 28.
    García-Martínez, C., Cordón, O., & Herrera, F. (2007). A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research, 180(1), 116–148.MATHCrossRefGoogle Scholar
  29. 29.
    Yagmahan, B., & Yenisey, M. M. (2008). Ant colony optimization for multi-objective flow shop scheduling problem. Computers and Industrial Engineering, 54(3), 411–420.CrossRefGoogle Scholar
  30. 30.
    Chan, F. T. S., Kumar, V. & Mishra, N. (2007). A CMPSO algorithm based approach to solve the multi-plant supply chain Problem. In Felix T.S. Chan & Manoj Kumar Tiwari (Ed.), Swarm Intelligence, Focus on Ant and Particle Swarm Optimization. Vienna, Austria: I-Tech Education and Publishing, ISBN: 978-3-902613-09-7.Google Scholar
  31. 31.
    Chong, C. S., Low, M. Y. H., Sivakumar, A. I. & Gay, K. L. (2006). A bee colony optimization algorithm to job shop scheduling. Proceedings of the 2006 Winter Simulation Conference. December 3−6, 2006. (pp. 1954–1961) Monterey, CA USA.Google Scholar
  32. 32.
    Chan, F. T. S., & Swarnkar, R. (2006). Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and Computer-Integrated Manufacturing, 22, 353–362.CrossRefGoogle Scholar
  33. 33.
    Deneubourg, J. L., Aron, S., Goss, S., & Pasteels, J. M. (1990). The self organizing exploratory pattern of the Argentine ant. Journal of Insect Behavior, 3, 159–168.CrossRefGoogle Scholar
  34. 34.
    Chan, F. T. S., & Kumar, N. (2009). Effective allocation of customers to distribution centres: a multiple ant colony optimization approach. Robotics and Computer-Integrated Manufacturing, 25, 1–12.CrossRefGoogle Scholar
  35. 35.
    Kawamura, H., Yamamoto, M., Suzuki, K. & Ohcuhi, A. (2000). Multiple ant colonies algorithm based on colony level interactions. Publication in the IEICE Transactions, Fundamentals, E83-A (Vol. 2, pp. 372–379).Google Scholar
  36. 36.
    Bullnheimer, B., Hartl, R. F., & Strauss, C. (1999a). Applying the ant systems to the vehicle routing problem. In S. Voss, S. Martello, I. H. Osman, & C. Roucairol (Eds.), Meta-Heuristics: Advances and Trends in Local search Paradigms for Optimization. (pp. 285–296), Dordrecht, Netherlands, Kluwer Academic Publishers.Google Scholar
  37. 37.
    Golden, B. & Stewart, W. (1985). Empiric Analysis of Heuristics in the Travelling Salesman Problem, E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy-Kan & D.B. Shmoys (Eds.), New York: Wiley.Google Scholar
  38. 38.
    Lawler, E. L., Lenstra, J. K., Rinnooy-Kan, A. H. G., & Shmoys, D. B. (1985). The Travelling Salesman Problem. New York: Wiley.Google Scholar
  39. 39.
    Dorigo, M., & Gambardella, L. M. (1997). Ant Colonies for the travelling salesman problem. BioSystems, 43, 73–81.CrossRefGoogle Scholar
  40. 40.
    Maniezzo, V., & Colorini, A. (1999). The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering, 11(5), 769–778.CrossRefGoogle Scholar
  41. 41.
    Ying, K. C., & Liao, C. J. (2003). An ant colony system approach for scheduling problems. Production Planning and Control, 14(1), 68–75.CrossRefGoogle Scholar
  42. 42.
    Goss, S., Beckers, R., Denebourg, J. L., Aron, S. & Pasteels, J. M. (1990) How trail laying and trail following can solve foraging problems for ant colonies. In R.N. Hughes (Ed.). Behavioural Mechanisms of Food Selection, NATO-ASI Series, (Vol. G 20, pp. 661–678) Berlin: SpringerGoogle Scholar
  43. 43.
    Gambardella, L. M. & Dorigo, M. (1996). Solving symmetric and asymmetric TSPs by ant colonies. In Proceedings of the IEEE Conference on the Evolutionary Computation (pp. 622–627).Google Scholar
  44. 44.
    Dorigo, M., Maniezzo, V. & Colorni, A. (1991). Positive Feedback as a Search Strategy, Technical report (pp. 91–106), Dipartimento di Elettronica, Politechnico di milano, Italy.Google Scholar
  45. 45.
    Colorni, A., Dorigo, M. & Maniezzo, V. (1991). Distributed optimization by ant colonies. In F. Vareladn & P. Bourgine (Eds.), Proceedings of European Conference on Artificial Life. (pp. 134–142) Paris, France: Elsevier Publishing.Google Scholar
  46. 46.
    Colorni, A., Dorigo, M. & Maniezzo, V. (1992). An investigation of some properties of an ant algorithm. R. Manner & B. Manderick (Eds.), In Proceedings of Conference on Parallel Problem Solving from Nature (pp. 509–520). Brussels, Belgium: Elsevier Publishing.Google Scholar
  47. 47.
    Gambardella, L.M., Dorigo, M. (1995). Ant-Q: A reinforcement learning approach to the travelling salesman problem. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 252–260).Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Vikas Kumar
    • 1
  • Nishikant Mishra
    • 2
  • Felix T. S. Chan
    • 3
  • Niraj Kumar
    • 4
  • Anoop Verma
    • 5
  1. 1.Department of ManagementDublin City University Business School DublinDublin 9Republic of Ireland
  2. 2.School of Management and Business, Aberystwyth UniversityAberystwythUK
  3. 3.Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHung HomChina
  4. 4.Department of ManagementSchool of Management, University of BathBathUK
  5. 5.Computer Aided Manufacturing Laboratory, Department of Mechanical EngineeringUniversity of CincinnatiCincinnatiUSA

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