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Comparison of Metaheuristic Approaches for Multi-objective Simulation-Based Optimization in Supply Chain Inventory Management

  • Lionel Amodeo
  • Christian Prins
  • David Ricardo Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)

Abstract

A Supply Chain (SC) is a complex network of facilities with dissimilar and conflicting objectives, immersed in an unpredictable environment. Discrete-event simulation is often used to model and capture the dynamic interactions occurring in the SC and provide SC performance indicators. However, a simulator by itself is not an optimizer. This paper therefore considers the hybridization of Evolutionary Algorithms (EAs), well known for their multi-objective capability, with an SC simulation module in order to determine the inventory policy (order-point or order-level) of a single product SC, taking into account two conflicting objectives: the maximization of customer service level and the total inventory cost. Different evolutionary approaches, such as SPEA-II, SPEA-IIb, NSGA-II and MO-PSO, are tested in order to decide which algorithm is the most suited for simulation-based optimization. The research concludes that SPEA-II favors a rapid convergence and that variation and crossover schemes play and important role in reaching the true Pareto front in a reasonable amount of time.

Keywords

Supply chain management Simulation Multi-objective Optimization Metaheuristics Evolutionary Algorithms 

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References

  1. 1.
    Amodeo, L., Chen, H., El Hadji, A.: Multi-objective supply chain optimization: An industrial case study. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 732–741. Springer, Heidelberg (2007)Google Scholar
  2. 2.
    Daniel, J.S., Rajendran, C.: A simulation-based genetic algorithm for inventory optimization in a serial supply chain. International Transactions in Operational Research 12, 101–127 (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Ding, H., Benyoucef, L., Xie, X.: A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Artificial Intelligence 19, 609–623 (2006)Google Scholar
  5. 5.
    Ettl, M., Feign, G.E., Lin, G.Y.: A supply network model with base-stock control and service requirements. Operations Research 48, 216–232 (2000)CrossRefGoogle Scholar
  6. 6.
    Gosavi, A.: Simulation-based optimization: parametric optimization techniques and reinforcement learning. Kluwer Academic Publishers, Boston (2003)CrossRefzbMATHGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of the IEEE Int. Conf. on Neural Networks, Piscataway, NJ, pp. 1942–1948. IEEE, Los Alamitos (1995)CrossRefGoogle Scholar
  8. 8.
    Köchel, P., Nieländer, U.: Simulation-based optimization of multi-echelon inventory systems. Int. Journal of Production Economics 93-94, 505–513 (2005)CrossRefGoogle Scholar
  9. 9.
    Schott, J.R.: Fault tolerant design using simple and multi-criteria genetic algorithms. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Boston, MA (1995)Google Scholar
  10. 10.
    Tripathi, P.K., Bandyopadhyay, S., Pal, S.K.: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Information Sciences 177, 5033–5049 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Voss, S., Woodruff, D.L.: Introduction to Computational Optimization Models for Production Planning in a Supply Chain, 2nd edn. Springer, Berlin (2006)zbMATHGoogle Scholar
  12. 12.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Zurich, Switzerland (2001)Google Scholar
  13. 13.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lionel Amodeo
    • 1
  • Christian Prins
    • 1
  • David Ricardo Sánchez
    • 2
  1. 1.ICD, LOSIUniversity of Technology of TroyesTroyesFrance
  2. 2.Centro de Optimización y Probabilidad Aplicada Universidad de los Andes; A.A. 4976Bogotá DC.Colombia

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