而且具体的车辆路径优化问题，往往在时间和路程两方面都有限制和要求，而在这方面的研究相对较少。
Faced to concrete routing problem, time and routs are both restricted and required and researches in this field are not enough.
第一阶段中采用分配规则将客户节点分配到各个仓库，将问题简化为多个单仓库的车辆路径优化子问题；
In the first stage the customers were assignmented to the depots and transformed the MDVRP to a set of CVRP problems;
车辆路径问题和网络流问题都是非常著名的网络优化问题。
The VRP and the network flow problem are all famous network optimization problems.
这对于应急物流车辆驾驶员来说，传统的静态路径优化已经不能满足应急的要求。
Therefore, for emergency logistics vehicle drivers, the traditional static route optimization has been unable to meet emergency requirements.
通过对物流车辆配送过程的分析，建立了带时间窗约束的物流配送路径优化问题的数学模型。
A mathematical model of logistics distribution route with time window was proposed according to the analysis of the logistics vehicle distribution process.
城市交通网络中出行车辆从起点到终点的路径优化问题是智能交通研究的重要课题。
The route optimization for the vehicle going from the origin to the destination in traffic network is an important issue in intelligent traffic research.
车辆路径问题是一类在物流配送调度中具有广泛应用的组合优化问题，属于强np难题。
Vehicle Routing problem (VRP) is a combination optimization problem in transportation logistics, which has been applied in many fields. It is a strong NP problem.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
在物流的各种优化问题中，有时间窗的车辆路径问题由于其巨大的经济效益，从它被提出以来就一直是业界研究的热点。
In kinds of the optimum problems of logistic, the vehicle routing problem with time window has been focused in the area because of its enormous benefit since it was proposed.
根据多相粒子群并行搜索的思想，给出mpso算法在带软时间窗物流配送车辆调度路径优化的实现流程。
By means of the idea of parallel search, the detailed procedure of the MPSO algorithm is given for solving vehicle scheduling problem with soft time Windows.
通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。
A PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator.
针对随机需求的多车辆路径问题（MVRPSD），提出了一种简单有效的重优化新算法。
Put forward a simply effective reoptimization algorithm for multiple vehicles routing problem with stochastic demand(MVRPSD).
设计了求解车辆路径问题的一种新的实数编码方案，将车辆路径问题转化成准连续优化问题，并采用罚函数法处理约束条件。
The VRP was changed into a quasi-continuous problem by designing a new real coding. Constrained terms in VRP were processed by the penalty function.
设计了求解车辆路径问题的一种新的实数编码方案，将车辆路径问题转化成准连续优化问题，并采用罚函数法处理约束条件。
The VRP was changed into a quasi-continuous problem by designing a new real coding. Constrained terms in VRP were processed by the penalty function.
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