Stochastic programming for flexible global supply chain planning

Yingjie Fan (), Frank Schwartz (), Stefan Voß () and David L. Woodruff ()
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Yingjie Fan: University of Hamburg
Frank Schwartz: University of Hamburg
Stefan Voß: University of Hamburg
David L. Woodruff: University of California

Flexible Services and Manufacturing Journal, 2017, vol. 29, issue 3, No 11, 633 pages

Abstract: 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.

Keywords: Supply chain risk management; Stochastic programming; Pyomo; PySP (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10696-016-9261-7

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