关键词: 产品回收定价, 逆向物流, 多频次回收, 车辆路径优化, GA-POS混合算法

Abstract: In order to overcome the short comings of the study of reverse logistics vehicle routing optimization in a reasonable combination of product recovery price adjustment and vehicle routing optimization scheduling, taking the intelligent recycling bin as the research object, and considering the multi-frequency recovery and vehicle sharing scheduling strategy, this paper proposed a reverse logistics vehicle routing optimization scheme based on product recovery pricing. First, this paper established a linear function between the collection quantity and the recycling price. Next, it established a reverse logistics operating cost model including shared vehicle transportation cost, the vehicle maintenance cost, and the penalty cost of the time window violation and environmental externality benefit, and proposed the maximum product profit model of the recycling center. Then, it designed a K-means clustering algorithm according to the characteristics of the model, to consider the space location, recycling frequency, and time window constraints of the intelligent recycling bin, and therefore proposed an improved genetic algorithm-particle swarm optimization (GA-PSO) hybrid algorithm which combined the strong global search ability of GA and the fast conergence speed of PSO. After that, it adopted the elite retention strategy to enhance the efficiency of the hybrid algorithm. A compaison of the hybrid genetic algorithm (HGA), genetic algorithm-tabu search (GA-TS) and hybrid ant colony optimization (HACO) verified the validity of the proposed model and algorithm. Fnally, it studied the proposed method based on a real-world case study of the intelligent reverse logistics network in Chongqing, China, and analyzed and discussed the recycling frequency and vehicle sharing at different product recovery pricing. The results show that the model and algorithm proposed n this paper can be used for effetively selection of the optimal pricing strategy. resource sharing of recycling vehicles, and reasonable vehicle routing optimization scheduling, and can effectively reduce the transportation cost of reverse logistics while maximizing the revenue of the recovery center, which can provide decision reference and method support for reverse logistics enterprises in the product recovery pricing strategy and vehicle routing optimization scheduling.

Key words: product recovery pricing, reverse logistics, multi-frequency recovery, vehicle routing optimization, GA-PSO（genetic algorithm-particle swarm optimization）hybrid algorithm