基于VNS-IWOA算法的定制公交出行调度与路径优化

Customized Bus Trip Scheduling and Route Optimization Based on NVS-IWOA Algorithm

摘要: 为研究定制公交系统的调度与线路集成优化问题，并充分考虑乘客需求特性，本文提出了一种定制公交与小汽车混合服务策略，旨在最小化系统运营成本和乘客出行成本。为此，构建了一个混合整数规划模型，用于优化车辆调度与路径优化。针对模型特点，设计了基于变邻域搜索的改进鲸鱼优化算法(VNS-IWOA)，并进行了算例求解。结果表明，结合小汽车与定制公交的混合服务模式使系统总成本降低了5.88%；在大规模算例求解中，VNS-IWOA算法在求解质量和收敛速度上均优于遗传算法(GA)和传统鲸鱼优化算法(IWOA)，系统成本分别降低了9.7%和8.9%，充分验证了VNS-IWOA算法的有效性和优越性。

Abstract: To address the scheduling and route integration optimization of customized bus systems while fully considering passenger demand characteristics, this study proposes a collaborative service strategy integrating customized buses and private cars. The objective is to minimize both system operating costs and passenger travel expenses. A mixed-integer programming model is developed to optimize vehicle routes, vehicle type selection, scheduling times, and passenger assignments. Given the model’s complexity, a Variable Neighborhood Search Improved Whale Optimization Algorithm (VNS-IWOA) is designed. Passenger demand was randomly generated on the road network in the Wuhan Economic and Technological Development Zone for extensive case testing. Results show that the collaborative service strategy reduces total system costs by 5.88%. For large-scale cases, the VNS-IWOA outperforms the Genetic Algorithm (GA) and the traditional Whale Optimization Algorithm (IWOA) in both solution quality and convergence speed, achieving system cost reductions of 9.7% and 8.9%, respectively, thus verifying the effectiveness and superiority of the VNS-IWOA approach.