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A hybrid scenario cluster decomposition algorithm for supply chain tactical planning under uncertainty

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  • Kazemi Zanjani, Masoumeh
  • Sanei Bajgiran, Omid
  • Nourelfath, Mustapha

Abstract

We propose a Hybrid Scenario Cluster Decomposition (HSCD) heuristic for solving a large-scale multi-stage stochastic mixed-integer programming (MS-MIP) model corresponding to a supply chain tactical planning problem. The HSCD algorithm decomposes the original scenario tree into smaller sub-trees that share a certain number of predecessor nodes. Then, the MS-MIP model is decomposed into smaller scenario-cluster multi-stage stochastic sub-models coordinated by Lagrangian terms in their objective functions, in order to compensate the lack of non-anticipativity corresponding to common ancestor nodes of sub-trees. The sub-gradient algorithm is then implemented in order to guide the scenario-cluster sub-models into an implementable solution. Moreover, a Variable Fixing Heuristic is embedded into the sub-gradient algorithm in order to accelerate its convergence rate. Along with the possibility of parallelization, the HSCD algorithm provides the possibility of embedding various heuristics for solving scenario-cluster sub-models. The algorithm is specialized to lumber supply chain tactical planning under demand and supply uncertainty. An ad-hoc heuristic, based on Lagrangian Relaxation, is proposed to solve each scenario-cluster sub-model. Our experimental results on a set of realistic-scale test cases reveal the efficiency of HSCD in terms of solution quality and computation time.

Suggested Citation

  • Kazemi Zanjani, Masoumeh & Sanei Bajgiran, Omid & Nourelfath, Mustapha, 2016. "A hybrid scenario cluster decomposition algorithm for supply chain tactical planning under uncertainty," European Journal of Operational Research, Elsevier, vol. 252(2), pages 466-476.
    • Handle: RePEc:eee:ejores:v:252:y:2016:i:2:p:466-476
    DOI: 10.1016/j.ejor.2016.01.048
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    References listed on IDEAS

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    Cited by:

    1. Dillon, Mary & Vauhkonen, Ilmari & Arvas, Mikko & Ihalainen, Jarkko & Vilkkumaa, Eeva & Oliveira, Fabricio, 2023. "Supporting platelet inventory management decisions: What is the effect of extending platelets’ shelf life?," European Journal of Operational Research, Elsevier, vol. 310(2), pages 640-654.
    2. Omid Sanei Bajgiran & Masoumeh Kazemi Zanjani & Mustapha Nourelfath, 2017. "Forest harvesting planning under uncertainty: a cardinality-constrained approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1914-1929, April.

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