A Multiobjective Model for Optimizing Green Closed-Loop Supply Chain Network under Uncertain Environment by NSGA-II Metaheuristic Algorithm

Nowadays, due to growing development and competitiveness in global markets of products, companies are forced to make significant efforts for supply procurement, production, and goods distribution in order to survive in the market and be able to respond to their customers’ needs as quickly and cost-e...

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Published in:Discrete dynamics in nature and society Vol. 2022; no. 1
Main Authors: Hassangaviar, Babak, Naderi, Bahman, Etebari, Farhad, Vahdani, Behnam
Format: Journal Article
Language:English
Published: New York Hindawi 2022
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Costs
Customers
Design
Distribution centers
Fuzzy sets
Garbage collection
Genetic algorithms
Global marketing
Heuristic methods
Linear programming
Literature reviews
Logistics
Manufacturers
Manufacturing
Mathematical models
Multiple objective analysis
Optimization
Optimization techniques
Sensitivity analysis
Sorting algorithms
Suppliers
Supply chain management
Supply chains
ISSN:1026-0226, 1607-887X
Online Access:Get full text
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Summary:Nowadays, due to growing development and competitiveness in global markets of products, companies are forced to make significant efforts for supply procurement, production, and goods distribution in order to survive in the market and be able to respond to their customers’ needs as quickly and cost-efficiently as possible. In this regard, supply chain management is considered a crucial indicator. This study presents a multiobjective, multifacility, closed-loop supply chain under uncertain environments considering green supply chain aspects. The model is designed with multiple products, periods, plants, customer markets, collection centers, recycle centers, distribution centers, return facilities, product recovery facilities, and suppliers. After modeling the study, the model is solved by the Nondominated Sorting Genetic Algorithms (NSGA-II) in order to rank the optimum solutions. The efficiency of the research model is indicated by the results and depicted graphs in the present study. Results show that the exact value of the triple objective functions is calculated. Also, the problem is solved in small, medium, and large dimensions. Then, the accuracy of the proposed model compared to the metaheuristic method is shown. Finally, by performing sensitivity analysis, we showed that target functions are less sensitive to reducing the capacity of centers.
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ISSN:1026-0226
1607-887X
DOI:10.1155/2022/2680892