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A robust optimization model for green supplier selection and order allocation in a closed-loop supply chain considering cap-and-trade mechanism

Hossein Mirzaee,Hamed Samarghandi,Keith A. Willoughby

Published 2022 in Expert systems with applications

ABSTRACT

Due to increasing air pollution, which is a consequence of the environmental effects of production in various industries, green supply chain management (GSCM) has attracted the attention of both scholars and practitioners. Green supplier selection is one of the most important problems in GSCM, which satisfies a firm's environmental goals as well as its economic targets. In this paper, for the first time, a green supplier selection problem considering both green and non-green evaluation criteria in a closed-loop supply chain is studied, and a cap-and-trade mechanism as a way of controlling the air pollution caused by manufacturers is proposed. To solve the described problem, a multi-objective robust optimization (RO) model, as an effective approach to handle uncertainty, is proposed. A numerical example using randomly generated data, accompanied by the analysis based on the proposed approach is elaborated to validate the presented model. The results prove that the developed model for green supplier selection is able to effectively enhance the decision-making process of the experts. By illustrating the trade-off in robustness between the model and proposed solutions, as well as the effect of the deviation penalty on the closeness of results to the achieved solution, we show how firms can make optimal decisions when assigning the parameters. Furthermore, analyses show that allowance amount (cap) and allowance prices in the cap-and-trade system impact the firms' costs and the amount of carbon released. Finally, we show that the cap-and-trade mechanism results in a better solution in terms of the total utility of the supply chain compared to the penalty-based system.

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