Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system

Soroush Avakh Darestani, Mehran Hemmati

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Supply chain of the perishable goods such as food material, diary, drugs, and blood products has drawn attention recently due to their impact on human lives. Supplying adequate and healthy food materials, drugs, and blood products and their management has always been one of the major concerns for humans Therefore, collection- and distribution-management of them, require comprehensive and accurate management and planning. The current research trend is to design a supply chain network for the perishable goods while taking into account uncertainties associated with their corruption. Queuing paradigm was utilized for wait-time reduction in the distribution centers. The objective function considered for the proposed model includes two sub-objectives: 1. To minimize the total network costs, and 2. To minimize greenhouse gas emissions. Some model parameters such as (demand, operational cost, goods transportation cost, and permissible capacity of the goods distribution center) were considered as uncertain parameters in order to control these uncertain parameters, an optimization method was used. Finally, for solving a two-objective model, three multi-criteria decision-making methods, namely overall weighting method, and Torabi-Hassini method were employed. The results indicate that there is a significant difference between the mean of the first, second objective functions and computational time. The TOPSIS method was used to select the most efficient method. Torabi-Hassani method rendered to be more effective to finding a solution
Original languageEnglish
Pages (from-to)277-292
Number of pages16
JournalComputers & Industrial Engineering
Volume136
Early online date10 Jul 2019
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes

Keywords

  • Perishable goods supply chain
  • Robust optimization
  • Uncertainty
  • Multi-objective decision-making methods

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