假设在一个供求关系系统中，车辆从货源取货，配送到对应的若干配送点。车辆存在最大载货量，且配送可能有时间限制。需要合理安排取货时间，组织适当的行车路线，使用户需求得到满足，同时使某个代价函数最小，比如总工作时间最少、路径最短等。

可以看出TSP问题是VRP问题的一种简单特殊形式。因此，VRP也是一种NP hard 问题。

目前解决此种问题的办法有多种，主要以启发式算法为主。包括退火算法、遗传算法、蚁群算法、禁忌算法等，本文介绍两种比较简单算法：C-W节约算法和遗传算法。(Assume that in a supply and demand relationship system, the vehicle picks up the goods from the source and delivers them to the corresponding distribution points. There is a maximum amount of cargo in the vehicle and there may be a time limit for delivery. It is necessary to arrange the picking time reasonably, organize the appropriate driving route, and satisfy the user's needs, and at the same time minimize a certain cost function, such as the minimum total working time and the shortest path.

It can be seen that the TSP problem is a simple and special form of the VRP problem. Therefore, VRP is also an NP hard problem.

At present, there are many ways to solve this kind of problem, mainly based on heuristic algorithms. Including annealing algorithm, genetic algorithm, ant colony algorithm, tabu algorithm, etc. This paper introduces two simple algorithms: C-W saving algorithm and genetic algorithm.)