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Amir-Mohammad Golmohammadi

Amir-Mohammad Golmohammadi

Academic rank: Assistant Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Engineering
Address: Arak University
Phone:

Research

Title
A state-of-the-art model of location, inventory, and pricing problem in the closed-loop supply chain network
Type
JournalPaper
Keywords
Closed-Loop Supply Chain Location Pricing Inventory & Carrying Policy Metaheuristic Genetic Algorithms Particle Swarm Optimization
Year
2022
Journal International Journal of Engineering
DOI
Researchers Amir-Mohammad Golmohammadi ، Rashed Sahraeian ، Parvaneh Haghshenas

Abstract

The main objective of designing the supply chain is to increase profitability. For this reason, a state-of-the-art model of a three-echelon closed-loop supply chain is proposed that consists of the manufacturer, retailer, and collection centers. For the first time, a new separate and autonomous channel is considered in this model for the sale of Reman products aiming at increasing the manufacturer's profitability. In this model, location, inventory, and pricing of the product are also taken into consideration. Lingo software is utilized to solve nonlinear objective functions at a small scale, and metaheuristic genetic algorithms and particle swarm optimization were utilized to solve at a large scale. The research results depict that the state-of-the-art model design of the closed-loop supply chain network is credible and the optimal location of supply chain components, optimal response of product flow, and product price are determined in a proper manner. As a result, the profitability of the whole closed-loop supply chain network increased. Sensitivity analysis of mathematical model depicts that this model shows higher sensitivity to retailer replacement factor, price and purchasing power of collection centers. Also, the genetic algorithm shows better performance on a large scale in terms of response quality. On the other hand, the time required by particle swarm optimization to reach a response is far better than the genetic algorithm. Ultimately, practical suggestions for the managers are presented considering the state-of-the-art model design of a closed-loop supply chain network.