4.7 Article

A Green Dual-Channel Closed-Loop Supply Chain Network Design Model

期刊

JOURNAL OF CLEANER PRODUCTION
卷 332, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2021.130062

关键词

Green supply chain; Closed-loop network design; Dual-channel supply chain; Multi-objective optimization; Particulate matter

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This article introduces a complex multi-objective optimization model for green dual-channel supply chain network design, aiming to optimize network flow by achieving economic and environmental objectives. By reducing CO2 emissions and particulate matter concentration, environmental goals are met, while minimizing overall costs also meets economic objectives.
Environmental considerations have become a significant issue in the design of supply chain networks due to today's increasing globalization trends. Therefore, supply chain network design needs to be managed in an efficient way to deal with the complex networks involved. The aim of this article is to present a multi-objective optimization model for a green dual-channel supply chain network that handles economic and environmental objectives to optimize network flow. A complex mixed-integer linear programming model (MILP) has been proposed in a green dual-channel and closed-loop supply chain (CLSC) network design. The main objective of the generated MILP model is to investigate the optimal selection of echelons and the optimal selection of transportation alternatives between these echelons in a CLSC network that includes an e-commerce channel structure based on economic and environmental considerations. Environmental aims are achieved by decreasing CO2 emissions and by reducing PM (particulate matter) concentration throughout the network. In addition, economic aims are also met by minimizing the overall cost. The validity of the presented model is supported by a case study in the home appliances industry. The results indicate that this model provides valuable knowledge and various alternatives to managers and policymakers depending on the different weight combinations.

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