【摘要】 为满足电子商务下的物流配送需求,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,在约束条件中增加时间约束、货物容积约束、车辆最大工作时间、多种车型、载重量限制和最大行驶距离等,以提高模型的适用性和通用性。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。即第一阶段用模糊分层聚类法将客户群分成若干区域,在每个区域又用扫描算法分解成若干符合约束条件的小规模子集;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,因此,采用改进混合遗传算法进行优化求解,最后的算例仿真表明了算法的有效性和可行性。更多还原

【Abstract】 In order to satisfy logistics distribution demand under electronic commerce,we modify the traditional vehcile scheduling model,amend objective function based on expense minimization,and add time restriction,goods capacity restriction,maximum vehicle working time,multi-types of vehicles,load capacity restriction,maximum running distance and so on into the restriction couditions,so as to improve the applicability and universality of model.Since vechile scheduling problem is NP puzzle,we get the optimal solution by improved two-phase algorithm.That is,in the first phase,the customer group is divided into some regions through fuzzy hierarchy clustering analysis method,and each region is also divided into some small scale sub-groups satisfying some restiction conditions through scanning algorithm.In the second phase,we optimize the line of each single TSPTW model according to to customer dot in each group.Therefore,improved hybrid genetic algorithm is used to get the optimal solution.Finally,computational tests demonstrate the efficiency and feasibility of our algorithm.更多还原