Dual-objective modeling and optimization of a low-carbon waste-classified collection problem

With improved quality of life, types of waste have become increasingly complex, and waste classification has become a global issue. As a result, the world is facing a waste-classified collection problem. However, the existing research on waste collection has paid little attention to waste classification. In this paper, we consider the pretreatment and classification of waste transfer stations. In recent years, global warming caused by carbon emissions has become a serious problem. Therefore, this work proposes the first dual-objective multi-depot two-echelon green vehicle routing system with various pickups to optimize waste-classified collection based on a mixed-integer programming model. To ensure the efficiency of our developed model, we designed a multiobjective brainstorming optimization algorithm with a novel clustering strategy based on the rank-clustering method and differential mutation. Compared with two classical multiobjective optimization algorithms in various generated test instances and a real-world case, the experimental results showed that the proposed model can help sanitation departments improve the economic and environmental benefits of waste-classified collection, and the proposed algorithm is an excellent optimizer for solving associated problems.