协同物流模式下多批次整车运输调度问题

Research on the Full-loaded Vehicle Routing Problem with Multiple Batches in Collaborative Transportation Mode

研究了协同物流模式下,单车库、多集散点的带时间窗以及单点多批次配送与集货请求的联合运输问题.考虑车辆的租用费用、行驶费用、未按时完成服务产生的惩罚费用等因素,建立了数学模型并提出了混合遗传算法.算法中加入了重启动机制,以提高种群质量和避免早熟收敛,并采用局部搜索策略以快速寻找最优值.数值实验证明,混合遗传算法求解该类运输调度问题具有良好的效果,且算法效率较高.

The vehicle routing problem with time windows as well as multiple pickups and deliveries was studied in collaborative transportation mode. A mathematical model was developed considering the factors such as the number of trucks rented, the distances covered, and the penalty due to service delay. A hybrid genetic algorithm was proposed accordingly, integrating the restarting scheme which is able to improve the initial population and avoid premature convergence respectively. In addition, the local research strategy was fully used to get the optimal value quickly. The results of the computational experiment indicate that the proposed algorithm is effective.