Abstract: Aiming at the deficiency of combining the research of reverse logistics vehicle routing optimization with dynamic customer demand processing and vehicle sharing scheduling, the strategies of transportation resource sharing and dynamic insertion were proposed, and the reverse logistics vehicle routing optimization problem based on dynamic demand and resource sharing was studied. Firstly, a dual objective optimization model that minimized the operating costs of reverse logistics and the number of collected vehicles was constructed. Secondly, a hybrid heuristic algorithm combining multi-objective particle swarm optimization algorithm and taboo search algorithm (MOPSO-TS) was designed to solve the model. The effectiveness of the proposed model and algorithm was verified through comparative analysis with NSGA-II, MOGA, and MOACO algorithms. Finally, based on the actual case of Chongqing, various operational indicators before and after optimization were compared and analyzed. The results show that the proposed model and algorithm can effectively reduce the total operating costs of reverse logistics and the number of collected vehicles. The research results can provide methodological references for optimizing reverse logistics vehicle paths based on dynamic demand and resource sharing and provide decision support for building efficient and low-cost reverse logistics networks.

Key words: traffic and transportation engineering; reverse logistics; vehicle routing optimization; dynamic demand; resource sharing; MOPSO-TS hybrid algorithm

摘要： 针对逆向物流车辆路径优化研究与动态客户需求处理和车辆共享调度相结合方面存在的不足，提出了运输资源共享和动态插入策略，研究了基于动态需求和资源共享的逆向物流车辆路径优化问题。首先，构建以逆向物流运营成本和收集车辆使用数最小化的双目标优化模型；其次，设计了一种结合多目标粒子群算法和禁忌搜索算法的混合启发式算法（MOPSO-TS）求解模型，通过与NSGA-Ⅱ、MOGA和MOACO等算法进行比较分析，验证了模型和算法的有效性；最后，结合重庆市实际案例，对比分析了优化前后的各项运营指标。结果表明，所提出的模型和算法可以有效降低逆向物流的运营总成本和收集车辆使用数。研究结果可为基于动态需求和资源共享的逆向物流车辆路径优化提供方法借鉴，并为构建高效率低成本的逆向物流网络提供决策支持。

关键词: 交通运输工程；逆向物流; 车辆路径优化; 动态需求; 资源共享; MOPSO-TS混合算法