Dual-objective modelling and optimization for a low-carbon waste classified collection problem

Abstract Recently, waste collection has become a research hotspot and an urgent problem to be solved due to the explosive growth of waste. With the improvement of the quality of life, there are various kinds of waste, which has caused great difficulties for waste collection. As a result, waste classified collection is proposed worldwide. However, the existing research on waste collection pays little attention to waste classification, and most of them aim to minimize the cost without consideration of it. In this paper, we consider the pretreatment and classification of waste transfer stations. In recent years, global warming caused by carbon emission has already become a serious problem. Therefore, this work proposes a dual-objective multi-depot two-echelon green vehicle routing problem with various pickups to optimize waste classified collection for the first time, and then we establish a mixed-integer programming model. To solve our investigated model effectively, we design a multi-objective brain storm optimization algorithm where a novel clustering strategy based on rank method and differential mutation are used. Compared with two classical multi-objective optimization algorithms on some generated test instances and a real-world case, the experimental results show that the proposed model helps sanitation departments improve economic and environmental benefits of waste classified collection, and the proposed algorithm is an excellent optimizer to solve the concerned problem