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Stochastic programming for flexible global supply chain planning

Author

Listed:
  • Yingjie Fan

    (University of Hamburg
    Xuzhou University of Technology)

  • Frank Schwartz

    (University of Hamburg)

  • Stefan Voß

    (University of Hamburg
    Pontificia Universidad Católica de Valparaíso)

  • David L. Woodruff

    (University of California)

Abstract

When ocean transportation is used, possible disruptions both at sea and on land should be taken into account in the planning process of the affected supply chain. In this paper, a framework to enable flexible global supply chain operational planning in stochastic environments is presented. In order to cope with unexpected events like natural or man-made disasters, flexible international long-distance transportation modes and postponement strategies are taken into account in our supply chain model. In order to balance supply chain costs and the flexibility of supply chains, a two-stage multi-scenario stochastic programming model is developed where the stochastic events are represented by corresponding scenarios. High quality solutions of all our problem instances are generated by using a Python based stochastic programming framework to solve the model. Finally, managerial insights related to flexible supply chain planning in stochastic environments are derived from our computational results.

Suggested Citation

  • Yingjie Fan & Frank Schwartz & Stefan Voß & David L. Woodruff, 2017. "Stochastic programming for flexible global supply chain planning," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 601-633, December.
    • Handle: RePEc:spr:flsman:v:29:y:2017:i:3:d:10.1007_s10696-016-9261-7
    DOI: 10.1007/s10696-016-9261-7
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