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)


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.


Supply chain management Simulation Multi-objective Optimization Metaheuristics Evolutionary Algorithms 


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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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