  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.
