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A strategic capacity allocation model for a complex supply chain: Formulation and solution approach comparison

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  • Li, Hongyan
  • Hendry, Linda
  • Teunter, Ruud

Abstract

In this paper, a capacity allocation problem is discussed based on a more complex supply chain than has been typically considered in previous quantitative modelling studies. This study analyses an integrated supply chain operation from raw material purchasing to final product distribution. The aim is to optimize the allocation of capacities among different facilities and product items. In this paper, a mixed integer programming model with dynamic characteristics is presented first, and then alternative solution procedures are introduced. The solution procedures include the development of a decomposition heuristic and an integrated heuristic algorithm. A computation study compares the solution procedures and uses sensitivity analysis to show that the heuristics work well. Thus, by adequately modelling a more realistic sized supply chain problem, this study represents an important advance in supply chain modelling research.

Suggested Citation

  • Li, Hongyan & Hendry, Linda & Teunter, Ruud, 2009. "A strategic capacity allocation model for a complex supply chain: Formulation and solution approach comparison," International Journal of Production Economics, Elsevier, vol. 121(2), pages 505-518, October.
    • Handle: RePEc:eee:proeco:v:121:y:2009:i:2:p:505-518
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    References listed on IDEAS

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    Cited by:

    1. Linda Zhang & Gang D.U. & Jun W.U. & Yujie M.A., 2020. "Joint production planning, pricing and retailer selection with emission control based on Stackelberg game and nested genetic algorithm," Post-Print hal-03276837, HAL.
    2. Linda Zhang & Carman Lee & Shuzhu Zhang, 2016. "An integrated model for strategic supply chain design: Formulation and ABC-based solution approach," Post-Print hal-01744868, HAL.
    3. Hong, Jiangtao & Diabat, Ali & Panicker, Vinay V. & Rajagopalan, Sridharan, 2018. "A two-stage supply chain problem with fixed costs: An ant colony optimization approach," International Journal of Production Economics, Elsevier, vol. 204(C), pages 214-226.
    4. Safaei, Mehdi, 2014. "An integrated multi-objective model for allocating the limited sources in a multiple multi-stage lean supply chain," Economic Modelling, Elsevier, vol. 37(C), pages 224-237.
    5. Mojtaba Akbari & Saber Molla-Alizadeh-Zavardehi & Sadegh Niroomand, 2020. "Meta-heuristic approaches for fixed-charge solid transportation problem in two-stage supply chain network," Operational Research, Springer, vol. 20(1), pages 447-471, March.
    6. Omid Abdolazimi & Farzad Bahrami & Davood Shishebori & Majid Alimohammadi Ardakani, 2022. "A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 10768-10802, September.
    7. Martin Steinrücke & Wolfgang Albrecht, 2018. "Integrated supply chain network planning and financial planning respecting the imperfection of the capital market," Journal of Business Economics, Springer, vol. 88(6), pages 799-825, August.

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