Environmental pollution and emissions, along with the increasing production and distribution of goods, have placed the future of humanity at stake. Today, measures such as the extensive reduction in emissions, especially of CO2 and CO, have been emphasized by most researchers as a solution to the problem of environmental protection. This paper sought to explore production routing problem in closed-loop supply chains in order to find a solution to reduce CO2 and CO emissions using the robust optimization technique in the process of product distribution. The uncertainty in some parameters, such as real-world demand, along with heterogeneous goals, compelled us to develop a fuzzy robust multi-objective model. Given the high complexity of the problem, metaheuristic methods were proposed for solving the model. To this end, the bee optimization method was developed. Some typical problems were solved to evaluate the solutions. In addition, in order to prove the algorithm’s efficiency, the results were compared with those of the genetic algorithm in terms of quality, dispersion, uniformity, and runtime. The dispersion index values showed that the bee colony algorithm produces more workable solutions for the exploration and extraction of the feasible region. The uniformity index values and the runtime results also indicated that the genetic algorithm provides shorter runtimes and searches the solution space in a more uniform manner, as compared with the bee colony algorithm.
