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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 an important problem in GSCM and seeks to satisfy 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 this particular problem, we propose a multi-objective robust optimization (RO) model. This specific model is an effective approach to handle uncertainty. A numerical example using randomly generated data, accompanied by subsequent discussion of the proposed approach, is deployed to validate the 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 decreasing the allowance amount (cap) and increasing the allowance prices in the cap-and-trade system escalate the firms' costs, but lower 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 for a specific range of carbon allowance. • This paper generalizes the green supplier selection and order allocation problem. • A mathematical model for the problem is developed. • A robust optimization approach to handle the model uncertainty is developed. • The impact of changes in model parameters on the objective functions is analyzed. • The cap-and-trade mechanism is compared with a penalty-based mechanism. [ABSTRACT FROM AUTHOR]

Abstract Given the high variability of both quantity and condition of the returns, closed-loop supply chains depend on a mix of remanufactured and new components. Effectively managing the combined flow of returned and new components is imperative to minimize total system costs. This paper proposes a model that helps manage the flow of returns by determining the incentives to offer returners, while simultaneously determining a capacity contract with the supplier of new components. The model considers returns from multiple sources, where each source has different characteristics in terms of the quantity and condition of its returns. The model also accounts for uncertainties from the return sources, as well as the new components supplier. Such uncertainties result in a failure to meet the demand for components, incurring loss costs. Sensitivity analyses of a numerical example were conducted to illustrate the model and gain further insights. The results demonstrate noteworthy relationships between supplier-related costs and those associated with not meeting the required demand, as well as their influence on the incentives to offer and the supplier's capacity contract. Highlights • Components can be sourced new or remanufactured from multiple return sources. • Model determines the capacity to contract from the new component supplier. • Model determines the optimal incentive for each return source. • Costs include losses due to a lack of remanufactured components. • Major interactions exist among incentives, contract capacity and loss costs. [ABSTRACT FROM AUTHOR]

Forward and reverse supply chains form a closed-loop supply chain. In this paper, a mathematical model is proposed for a closed-loop supply chain network by considering global factors, including exchange rates and customs duties. The model is a multi-objective mixed-integer linear programming model under uncertain demand. A solution approach based on fuzzy programming is developed for solving the optimization problem. The model is then applied in a network, which is located in Southwestern Ontario, Canada. A sensitivity analysis is provided to validate the model. This model considers global factors, multiobjectives, and uncertainty simultaneously in a closed-loop supply chain network. [ABSTRACT FROM AUTHOR]