Robust optimization to design a four-echelon perishable supply chain under stochastic deterioration rate: A case study.

Document Type : Research Paper

Authors

1 Department of Industrial Management, Kish Campus, University of Tehran, Kish, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

3 Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

Abstract

The perishable dairy industry has to deal with multiple challenges such as demand forecasting, price fluctuations, lead time, and inflated orders along with difficulties of climatic and traffic conditions, storage areas and shipment in unfavorable circumstances. This research introduces a robust bi-level mathematical model to optimize a multi-echelon Perishable Supply Chain (PSC. To this end, integrated multi-objective Mixed Integer Linear Programming (MILP) models are developed to formulate the problem. stochastic deterioration rate is taken into account as the main factor that determines model performance due to perishability of products. In order to contribute to the literature, mainly by addressing uncertainty and perishability, a solution technique based on robust programming and -constrait approach is developed to accommodate suggested bi-level model. This technique can deal with problem uncertainty while also ensuring the robustness of the overall system. Sensitivity analysis is implemented along with three well-known quality indicators to assess the performance of the proposed solution method and quality of obtained solutions. Finally, real case study is provided using the CPLEX solver to showcase the applicability of the proposed methodology and discuss the complexity of the model. Results demonstrate the efficiency of the proposed methodology in finding optimal solutions.

Keywords

Main Subjects

References

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Journal of Industrial and Systems Engineering
Volume 15, Issue 1 - Serial Number 1
January 2023
Pages 134-158

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History

  • Receive Date: 21 July 2022
  • Revise Date: 25 October 2022
  • Accept Date: 16 November 2022

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