摘要
电子商务物流企业面临的是多批次、小批量、时间要求高、需求个性化的现代化市场,为了提高模型的适用性和通用性,将传统车辆调度模型进行修改,将目标函数改为基于费用最小,在约束条件中增加最大工作时间、多类车型、车辆载重量限制和最大行驶距离。由于有时间窗的车辆调度问题是NP难问题,采用改进两阶段算法进行求解。即第一阶段用K-means将客户群分成若干区域;第二个阶段对各个分组内客户点,就是一个个单独TSPTW模型的线路优化问题,采用混合遗传算法进行优化求解,最后,结合具体实例,证明该改进算法的良好性能。
To the problem that the logistics company of electronic commerce will face the modem market with multiple batches, small volume, high time requirement and individuation demand, the traditional vehicle scheduling model is modified in order to reduce the distribution cost. Objective function is modified based on minimum expense. And the maximum work time, multi-vehicle types, vehicle load capacity restrictions, and maximum running distance are added in restraint conditions to improve the applicability and universal characteristics of model. For vecbile scheduling problem is NP puzzle, the optimization solution is obtained by the improved two-phase algorithm. In the first phase, the customer group is divided into some regions through K-means clusteing analysis method. In the second phase, the line of each single TSPTW model is optimized according to customer dot in each group. The hybrid genetic algorithm is used to get the optimization solution. The good performance of algorithm can be proved by experiment calculation and concrete examples.
出处
《控制工程》
CSCD
2008年第5期489-492,共4页
Control Engineering of China
基金
西部交通科技基金资助项目(200439800063)
黑龙江省教育厅基金资助项目(11521213)
黑龙江省科技攻关基金资助项目(GB05D202-3)
