International Journal of Industrial Engineering and Management

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Vol. 13 No. 2 (2022)
Original Research Article

A Novel Mathematical Model To Design an Agile Supply Chain for Perishable Products

Olga Anichkina
K.G. Razumovsky Moscow State University of technologies and management
Bio
Tzu-Chia Chen
Krirk University
Bio
Stanislav I. Sivakov
Belgorod State University
Bio
Olga Yuryevna Voronkova
Altai State University
Bio
Sergey Alekseevich Gorovoy
Kuban State Agrarian University named after I.T. Trubilin, Krasnodar
Bio
Alla Andronikovna Davidyants
Sechenov First Moscow State Medical University, Department of Propaedeutics of Dental Diseases, Moscow
Bio

Published 2022-06-30

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Keywords

  • Supply chain,
  • Agility,
  • Risk,
  • Flexibility,
  • Fuzzy goal programming

How to Cite

Anichkina, O., Chen, T.-C., Sivakov, S. I., Voronkova, O. Y., Alekseevich Gorovoy, S., & Andronikovna Davidyants, A. (2022). A Novel Mathematical Model To Design an Agile Supply Chain for Perishable Products. International Journal of Industrial Engineering and Management, 13(2), 88–98. https://doi.org/10.24867/IJIEM-2022-2-303

Abstract

This paper proposes a novel multi-objective mathematical model to design a multi-product agile supply chain (ASC) in which one of the most useful utilities in the field of no waste system is considered. The proposed ASC network include different levels of plants, warehouse facilities, distribution facilities, customers in the forward logistics flow, and collection, repair, recycling and disposal facilities for the reverse logistics flow. The objective functions are to minimize the total cost, minimization of lead time, minimization of risk and maximization of flexibility. As the model is multi-objective, the fuzzy goal programming (FGP) method is then applied to deal with the multi-objectiveness of the model. To validate the proposed model, the GAMS software and CPLEX solver is used to solve several test problem instances in different sizes. These problems are then analysed under different conditions using sensitivity analyses. It is revealed that the proposed model has the appropriate performance to obtain efficient optimal solutions.

 

Article history: Received (November 23, 2021); Revised (January 01, 2022); Accepted (January 26, 2022); Published online (March 4, 2022)

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