摘要
实时订货信息下的车辆调度是随机性车辆调度中货物需求量、需求点均不确定的情况下的车辆调度.针对该问题,本文构建了配送总成本最小的目标函数,提出了采用混合算法求解的思路.即以局部搜索法求得初始解,采用遗传算法优化初始解,并在送货时间更新后,利用禁忌搜索法求解速度快的特点改进调度方案,得到订货信息不断更新的条件下的车辆调度方案.通过实例分析,本方法既可解决电子商务条件下实时订货的车辆调度问题,也具有求解结果可靠、求解过程快速的特点.
Vehicle scheduling optimization of real-time demand information is a kind of random vehicle scheduling which is in the case of the uncertain demand of the goods and uncertain demand points. Therefore, the objective function of the lowest total distribution cost is established and the combinational algorithm is presented to solve the problem. In other words, the initial results can be found by the local search algorithm and modified by the genetic algorithm. The scheduling will be improved by the Tabu algorithm as soon as the delivery time is updated. An application example is proposed to show that the combinational algorithm can solve the real-time demand scheduling problem under electronic commerce conditions more reliably and more efficiently.
出处
《应用数学与计算数学学报》
2012年第1期53-65,共13页
Communication on Applied Mathematics and Computation
基金
国家"八六三"高技术研究发展计划资助项目(2008AA11Z201)
湖南省自然科学基金资助项目(11JJ6044)
长沙理工大学道路灾变防治及交通安全教育部工程研究中心开发基金资助项目(KFJ080308)
关键词
实时订货信息
车辆调度问题
局部搜索法
遗传算法
禁忌搜索法
real-time demand information
vehicle scheduling problem （VSP）
local search algorithm
hybrid genetic algorithm
Tabu search algorithm
