摘要：物流行业涉及公路运输，铁路运输，航空运输，航海运输，在国民经济中发挥着举足轻重的作用。其中路径规划是节约物流运输成本的主要途径，是提高物流配送企业竞争力的重要方式。

物流配送路径规划问题可抽象为VRP，且VRP是TSP的扩展，两者均是NP-hard问题。由于启发式算法可高效求解NP-hard问题的近似最优解，故本文研究遗传算法、模拟退火算法和蚁群算法实现路径规划。不过遗传算法、模拟退火算法和蚁群算法在路径规划应用中存在局限性，本文首先研究TSP问题，分别实现三种算法的优化。优化包括：在遗传算法中采用基于最小生成树的交叉算子，指定算法的寻优方向；在模拟退火中添加升温过程，扩大算法的搜索范围，避免算法过早收敛于局部最优解；在蚁群算法中加入局部信息素更新过程，避免局部路径被反复搜索，提高算法的全局搜索能力。然后对比分析优化前后算法求解TSP的结果，验证优化操作的合理性与有效性。接着将优化的启发式算法应用到CVRP中，进而得出三种路径规划方案，并推选出成本最小的物流配送路径方案。

关键词：VRP；优化；交叉算子；升温；信息素局部更新

Abstract：Logistics industry involved in road transport, rail transport, air transport, maritime transport, in the national economy plays a pivotal role. Path planning is the main way to save the cost of logistics and transportation, and it is an important way to improve the competitiveness of logistics and distribution enterprises.

Logistics distribution path planning problem can be abstracted as VRP, and VRP is TSP expansion, both of which are NP-hard problems. Because the heuristic algorithm can solve the approximate optimal solution of NP-hard problem efficiently, the genetic algorithm, simulated annealing algorithm and ant colony algorithm are used to realize path planning.

But the limitations of genetic algorithm, simulated annealing algorithm and ant colony algorithm in path planning application, so this paper first studies TSP problem, and realizes the optimization of three algorithms respectively. The optimization includes the use of the crossover operator based on the minimum spanning tree in the genetic algorithm to specify the optimal direction of the algorithm; In the simulated annealing to add the heating process, to expand the search range of the algorithm, and to avoid premature convergence of the algorithm in the local optimal solution; The local pheromone updating process is added to the ant colony algorithm, avoid local paths being searched repeatedly, improve the global search ability of the algorithm. And then compare the results of the TSP obtained by the non- optimal algorithm and optimal algorithm, verify the rationality and effectiveness of the optimization operation. The next step, the optimized heuristic algorithm is applied to CVRP, then three path planning schemes are obtained, and select the lowest cost of logistics distribution path scheme.

Keywords: VRP; optimization; crossover operator; heating process; pheromone local renewal

目 录

第一章 绪论 1

1.1智能路径规划的研究背景与意义 1

1.2车辆路径问题的研究历史与成果 3

1.2.1国外研究历史与成果 3

1.2.2国内研究历史及成果 4

1.3主要研究内容与框架结构 5

第二章 路径规划问题概述 7

2.1旅行商问题（TSP）