Two Stage Stochastic Programing Based on the Sample Average Approximation and Accelerated Benders Decomposition for Designing Closed-loop Supply Chain Network Design under Uncertainty

Document Type : Research Paper

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Abstract

In this paper, a comprehensive mathematical model for designing supply chain network via considering integrated flow of forward and reverse of multiple products during multiple periods is proposed. The uncertainty of the parameters includes customer demand, products reverse flows, rates of reverse product recovery and disposal, as well as costs of products transportation, storage and reverse flow management are considered via two stage stochastic programming. An efficient solution algorithm based on the sample average approximation and new accelerated benders decomposition is developed to solve the proposed model. An accelerated Benders decomposition algorithm utilizing efficient acceleration mechanisms based on the priority heuristic and adding the demand constraint to master problem is devised to cope with computational complexity. Computational analysis is also provided by using a phone cell industrial case study to present the significance of the proposed stochastic model versus deterministic one as well as the efficiency of the proposed the accelerated benders decomposition algorithm. In addition, obtained solutions of the stochastic model have a more robustness than solutions of the deterministic model.

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References

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Journal of Modeling in Engineering
Volume 15, Issue 49
September 2017
Pages 217-234

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History

  • Receive Date: 10 June 2015
  • Revise Date: 09 September 2015
  • Accept Date: 27 September 2015

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