Hybrid algorithm for discrete event simulation based supply chain optimization

  • Authors:

  • Taejong Yoo;Hyunbo Cho;Enver Yücesan

  • Affiliations:

  • Department of Industrial Engineering, Pohang University of Science and Technology, San 31 Hyoja, Pohang 790-784, Republic of Korea;Department of Industrial Engineering, Pohang University of Science and Technology, San 31 Hyoja, Pohang 790-784, Republic of Korea;Technology and Operations Management Area, INSEAD, Boulevard de Constance 77305, Fontainebleau Cedex, France

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

Supply chain optimization, as a key determinant of strategic resources mobility along the value-added chain, allows each participant in the global network to capitalize on its particular strategic competency. Simulation is widely used to test the impact on supply chain performance for the strategic level decisions, such as the number of plants, the modes of transport, or the relocation of warehouses. However, the complexity of supply chain optimization problem and the stochastic nature of simulation cause the unaffordable computational load; the evaluation of a large number of alternatives for supply chain optimization is in a class of NP-hard problem and the number of simulation replications is required for accurately evaluating the performance of each alternative. The objective of the present work is to propose hybrid algorithm with the application of the nested partitioning (NP) method and the optimal computing budget allocation (OCBA) method to reduce the computational load, hence, to improve the efficiency of supply chain optimization via discrete event simulation. The NP method is a global sampling strategy that is continuously adapted via a partitioning of the feasible solution region. The number of candidate alternatives to be evaluated can be reduced by the application of NP. The OCBA method minimizes the number of samples (simulation replications) required to evaluate a particular alternative by allocating computing resources to potentially critical alternative. Carefully designed experiments show extensive numerical result to illustrate the benefits of the proposed approach.