摘 要

本文使用了K均值聚类和秩和比综合评价法为课题研究建立了车辆路径规划模型，而模型的求解使用了智能算法中的蚁群算法，蚁群算法具有高效的搜索能力，且不容易使求得的解是局部最优解。

论文主要研究了药品配送中的车辆运输路径规划的问题，目的是求得在药品配送中各个车辆的最佳运输路径，这个问题的研究具有重要的实用价值，在多配送中心多车型的物流运输问题中都能够有所应用。结合案例本文的研究在K均值聚类和秩和比综合评价的得出的结果基础上用蚁群算法求解模型，成功的求解到了药品配送时的车辆运输路径图。

研究结果表明：药品配送网络研究问题归结为车辆路径问题是合理的，使用此类模型求解可以得到一个比较优化的车辆物质运输路径。蚁群算法在解决本研究课题中起到了较好的效果。但是从结果上分析，模型的建立并不完善，出现了车辆不足的情况没有考虑进模型和算法中。

本文的特色是：将多配送中心问题分解为多个单配送中心问题的组合，使得问题的求解变得简单。另外，通过对初步结果的分析，发现了车辆不足的问题，进而对模型和算法进行了改进，先判断现有车辆是否充足，不足则进行添加，最后进行求解。

关键词： K均值聚类；秩和比综合评价法；蚁群算法

Abstract

This article uses the K-means clustering, and rank and established a research vehicle routing model than the comprehensive evaluation method, for solving the model and the use of intelligent algorithms ant colony algorithm, ant colony algorithm is an efficient search capability, not easily so obtained solution is locally optimal solution.

Thesis, the problem of drug distribution vehicle transport path planning, the purpose is to obtain the best transportation route for each vehicle, the study of this problem has important practical value in drug distribution, and distribution centers in a multi-multi-model transport logistics problems can be applied. With case studies in this paper, and a rank based K-means clustering and Ratio Comprehensive Assessment of the results obtained with the ant colony algorithm model, successfully solving a vehicle to transport the road map when drug distribution.

The results show that: Drug Distribution Network Problems attributed to vehicle routing problem is reasonable to use such a model to solve the mass transport vehicle can get a more optimal path. Ant colony algorithm has played a good effect in solving the problem in the present study. But from the results of analysis, the model is not perfect, there has been insufficient vehicle without taking into account the model and algorithm.