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Simulation-based optimization for material handling systems in manufacturing and distribution industries

  • Chris S. K. Leung
  • Henry Y. K. Lau
Article
  • 31 Downloads

Abstract

Faced with rising customer expectations, shortening product life cycle and increasing number of competitors, many companies are paying increasing attention to streamline their operations with a view to improving the timeliness in response to changes and demands in their supply chains. Material handling is a vital element of the supply chains, which involves a variety of operations including the movement, storage, protection and control of materials and products throughout the processes of manufacturing and distribution. Having efficient material handling systems is of great importance to these industries for maintaining and facilitating a continuous flow of materials through the workplace and guaranteeing that required materials are available when needed. In this paper, we apply a simulation-based optimization framework for solving several real-life single objective and multi-objective optimization problems. The results reveal that the simulation-based optimization framework could become an effective decision making tool for solving both single and multi-objective optimization problems in manufacturing and distribution industries and help management to find near-optimal system parameters to fulfill important objectives.

Keywords

Artificial immune system Artificial intelligence Material handling system Optimization Simulation 

Notes

Acknowledgements

This article is a revised and expanded version of a paper entitled “A Multi-objective Simulation-based Optimization Approach applied to Material Handling System” presented at the 2nd EAI International Conference on Computer Science and Engineering (COMPSE 2018), Furama Hotel, Bangkok, Thailand, March 2018. All authors have seen and approved the manuscript and have contributed significantly for the paper.

References

  1. 1.
    Banks, J. S., Carson, I., Nelson, B. L., & Nicol, D. M. (2010). Discrete-event system simulation (4th ed.). Upper Saddle River: Prentice Hall.Google Scholar
  2. 2.
    Rosen, S. L. (2003). Automated simulation optimization of systems with multiple performance measures through preference modeling. State College: Pennsylvania State University.Google Scholar
  3. 3.
    Leung, C. S. K., & Lau, H. Y. K. (2011). An optimization framework for modeling and simulation of dynamic systems based on AIS. In International federation of automatic control world congress, Italy, International Federation of Automatic Control (IFAC), p. 11608.CrossRefGoogle Scholar
  4. 4.
    Leung, C. S. K., & Lau, H. Y. K. (2016) A hybrid multi-objective immune algorithm for numerical optimization. In A. J. Filipe (Ed.), The 8th international joint conference on computational intelligence, Porto, Portugal, Vol. 3: ECTA, Scitepress ,pp. 105–114.Google Scholar
  5. 5.
    Burnet, F. M. (1959). The clonal selection theory of acquired immunity. Nashville: Vanderbilt University.CrossRefGoogle Scholar
  6. 6.
    Jerne, N. K. (1974). Towards a network theory of the immune system. Annual Immunology (Paris), 125(C)(1–2), 373–389.Google Scholar
  7. 7.
    Ding, H., Benyoucef, L., & Xie, X. (2004). A simulation-based optimization method for production-distribution network design. In Proceedings of the IEEE international conference on systems, man and cybernetics, 10–13 October, Vol. 5, pp. 4521–4526.Google Scholar
  8. 8.
    Elahi, M. M. L., Záruba, G. V., Rosenberger, J., & Rajpurohit, K. (2009). Modeling and simulation of a general motors conveyor system using a custom decision optimizer. technical REPORT. Arlington: University of Texas at Arlington.Google Scholar
  9. 9.
    Kuo, R. J., & Yang, C. Y. (2011). Simulation optimization using particle swarm optimization algorithm with application to assembly line design. Applied Soft Computing, 11(1), 605–613.  https://doi.org/10.1016/j.asoc.2009.12.020.CrossRefGoogle Scholar
  10. 10.
    Subulan, K., & Cakmakci, M. (2012). A feasibility study using simulation-based optimization and Taguchi experimental design method for material handling—Transfer system in the automobile industry. The International Journal of Advanced Manufacturing Technology, 59(5), 433–443.  https://doi.org/10.1007/s00170-011-3514-0.CrossRefGoogle Scholar
  11. 11.
    Chang, K. H., Chang, A. L., & Kuo, C. Y. (2014). A simulation-based framework for multi-objective vehicle fleet sizing of automated material handling systems: an empirical study. Journal of Simulation, 8(4), 271–280.  https://doi.org/10.1057/jos.2014.6.CrossRefGoogle Scholar
  12. 12.
    Lin, J. T., & Huang, C.-J. (2014). Simulation-based evolution algorithm for automated material handling system in a semiconductor fabrication plant. In E. Qi, J. Shen, & R. Dou (Eds.), Proceedings of 2013 4th international Asia conference on industrial engineering and management innovation (IEMI2013), Berlin, Heidelberg, pp. 1035–1046.  https://doi.org/10.1007/978-3-642-40060-5_99.Google Scholar
  13. 13.
    Xiang, L., Qing-xin, C., Ai-lin, Y., & Hui-yu, Z. (2016). Simulation optimization of manufacturing system including assemble lines and material handling systems. In L. Zhang, X. Song, & Y. Wu (Eds.), Theory, methodology, tools and applications for modeling and simulation of complex systems: 16th Asia simulation conference and SCS autumn simulation multi-conference, AsiaSim/SCS AutumnSim 2016, Beijing, China, October 8–11, 2016, Proceedings, Part II, Singapore, Springer Singapore, pp. 63–70.Google Scholar
  14. 14.
    de Castro, L. N., & Von Zuben, F. J. (2000). The clonal selection algorithm with engineering applications. In Proceedings of the genetic and evolutionary computation conference, Las Vegas, pp. 36–37.Google Scholar
  15. 15.
    de Castro, L. N., & Timmis, J. (2002). An artificial immune network for multimodal function optimization. In The 2002 congress on evolutionary computation, Vol. 1, pp. 699–704.Google Scholar
  16. 16.
    Ye, W., Feng, W., & Fan, S. (2017). A novel multi-swarm particle swarm optimization with dynamic learning strategy. Applied Soft Computing, 61(Supplement C), 832–843.  https://doi.org/10.1016/j.asoc.2017.08.051.CrossRefGoogle Scholar
  17. 17.
    Javidrad, F., & Nazari, M. (2017). A new hybrid particle swarm and simulated annealing stochastic optimization method. Applied Soft Computing, 60(Supplement C), 634–654.  https://doi.org/10.1016/j.asoc.2017.07.023.CrossRefGoogle Scholar
  18. 18.
    Jamrus, T., Chien, C. F., Gen, M., & Sethanan, K. (2017). Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, PP(99), 1.  https://doi.org/10.1109/tsm.2017.2758380.CrossRefGoogle Scholar
  19. 19.
    Ali, A. F., & Tawhid, M. A. (2017). A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems. Ain Shams Engineering Journal, 8(2), 191–206.  https://doi.org/10.1016/j.asej.2016.07.008.CrossRefGoogle Scholar
  20. 20.
    Chen, K., Zhou, F., & Liu, A. (2018). Chaotic dynamic weight particle swarm optimization for numerical function optimization. Knowledge-Based Systems, 139(Supplement C), 23–40.  https://doi.org/10.1016/j.knosys.2017.10.011.CrossRefGoogle Scholar
  21. 21.
    Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In The 6th international conference on parallel problem solving from nature, Springer, pp. 849–858.Google Scholar
  22. 22.
    Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103. Zurich: Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH).Google Scholar
  23. 23.
    Gong, M., Jiao, L., Du, H., & Bo, L. (2008). Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, 16(2), 225–255.  https://doi.org/10.1162/evco.2008.16.2.225.CrossRefGoogle Scholar
  24. 24.
    Destro, R. d. C., & Bianchi, R. A. C. (2015). Incorporating hybrid operators on an immune based framework for multiobjective optimization. In 2015 IEEE international conference on systems, man, and cybernetics (SMC), 9–12 October 2015, pp. 2809–2816.  https://doi.org/10.1109/smc.2015.490.
  25. 25.
    Liu, R., Li, J., Fan, J., Mu, C., & Jiao, L. (2017). A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization. European Journal of Operational Research, 261(3), 1028–1051.  https://doi.org/10.1016/j.ejor.2017.03.048.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Atashpendar, A., Dorronsoro, B., Danoy, G., & Bouvry, P. (2018). A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization. Journal of Parallel and Distributed Computing, 112, 111–125.  https://doi.org/10.1016/j.jpdc.2017.05.018.CrossRefGoogle Scholar
  27. 27.
    Lučic, P., & Teodorovic, D. (1999). Simulated annealing for the multi-objective aircrew rostering problem. Transportation Research Part A: Policy and Practice, 33(1), 19–45.  https://doi.org/10.1016/S0965-8564(98)00021-4.CrossRefGoogle Scholar
  28. 28.
    Baumgartner, U., Magele, C., & Renhart, W. (2004). Pareto optimality and particle swarm optimization. IEEE Transactions on Magnetics, 40(2), 1172–1175.  https://doi.org/10.1109/tmag.2004.825430.CrossRefGoogle Scholar
  29. 29.
    Syberfeldt, A., Grimm, H., Ng, A., Andersson, M., & Karlsson, I. (2008). Simulation-based optimization of a complex mail transportation network. In Proceedings of the 2008 winter simulation conference, Miami, FL, USA, pp. 2625–2631.Google Scholar
  30. 30.
    Pareto, V. (1896). Cours d’Économie Politique (Vol. 1). Lausanne: F. Rouge.Google Scholar
  31. 31.
    Pareto, V. (1897). Cours d’Économie Politique (Vol. 2). Lausanne: F. Rouge.Google Scholar
  32. 32.
    Flexsim Software Products Inc. (2016). www.flexsim.com. Accessed 1 July 2016.
  33. 33.
    Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRefGoogle Scholar
  34. 34.
    S.F. Express (Hong Kong) Limited. (2016). http://www.sf-express.com/hk/tc/. Accessed 16 April 2016.
  35. 35.
    Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.Google Scholar
  36. 36.
    Schwefel, H. P. (1975). Binäre Optimierung durch Somatische Mutation. Technical Report.Google Scholar
  37. 37.
    Coello Coello, C. A., & Cortés, N. C. (2005). Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6(2), 163–190.  https://doi.org/10.1007/s10710-005-6164-x.CrossRefGoogle Scholar
  38. 38.
    Van Veldhuizen, D. A. (1999). Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio.Google Scholar
  39. 39.
    Schott, J. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongPok Fu LamChina

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