基于改进蚁群算法的农资化肥物流运输路线研究

发布时间：2018-04-12 18:20

本文选题：农资物流 + 运输路线优化　；参考：《吉林大学》2014年硕士论文

【摘要】：经济的快速发展带动了各行各业的发展，物流业发展表现最为突出。作为第三利润源泉的物流业受到各大企业的关注。作为传统的农业大国，“三农”问题一直是我国关注的重点问题。影响农业发展的农资物流也成为物流业中的重点。农资物流的效率直接影响到农民的收成，农业的产量、农村的稳定。农资流通体系的完善是实现农业现代化的重要环节，有利于提高农业经济效益。但目前我国的农资流通方式和水平比较落后，不能满足现代农业生产和现代市场经济的要求，因而推进农资流通网络建设，完善流通体系很有必要。我国物流技术专业化水平有很大的进步，但与国外先进的物流运输服务还有很大的差距。物流路线选择优化问题中运输车辆路线问题是目前物流领域研究重点。如何选择物流运输路线，优化已有的运输路线，成为众多学者研究的重点。越来越多的学者致力研究用于解决车辆路线问题的各种智能算法，并在取得良好效果。本文旨在针对A农资公司化肥运输问题选择一条最优路线，在遍历所有运输结点的前提下，使运输路线最短，，满载率最高。本文采用改进的节约算法和改进蚁群算法分别求解，并比较分析两种算法得出的结果，最终得出改进蚁群算法求解的结果不仅节约了里程和车辆数，还提高了车辆的满载率。首先,本文介绍了相关理论知识，主要有物流运输理论，农资物流的概念、发展现状和改善措施，以及车辆路线问题的概念、构成要素、数学模型等。其次，本文分析了物流运输路线选择优化常用方法，主要有精确算法、启发式算法、智能算法三类，并详细说明了具体算法的基本原理、优缺点及适应范围，重点介绍了节约算法和改进的节约算法，蚁群算法和改进的蚁群算法。再次，以A农资公司化肥运输为实例，分别基于改进的节约算法和改进蚁群算法进行求解，并比较分析求解结果，得出改进蚁群算法对于解决车辆路线优化问题更有优势。最后，对全文进行总结，分析本文存在的不足。车辆路线问题的动态性使得问题更加复杂，但仍然有很高的研究价值。
[Abstract]:The rapid development of economy has driven the development of various industries, the logistics industry development performance is the most outstanding.As the third source of profit, the logistics industry is concerned by the major enterprises.As a traditional agricultural country, the issue of agriculture, rural areas and farmers has always been the focus of our attention.Agricultural material logistics, which affects the development of agriculture, has also become the focus of the logistics industry.The efficiency of agricultural material logistics has a direct impact on farmers' harvest, agricultural output and rural stability.The perfection of agricultural circulation system is an important link to realize agricultural modernization, which is helpful to improve agricultural economic benefit.However, at present, the mode and level of agricultural material circulation in our country are relatively backward, which can not meet the requirements of modern agricultural production and modern market economy, so it is necessary to promote the construction of agricultural material circulation network and perfect the circulation system.The specialization level of logistics technology in our country has made great progress, but there is still a big gap with foreign advanced logistics transportation service.In the optimization of logistics route selection, the transportation vehicle routing problem is the focus of logistics research.How to choose the logistics transportation route and optimize the existing transportation route has become the focus of many scholars.More and more scholars are studying various intelligent algorithms to solve vehicle routing problems, and good results have been obtained.The purpose of this paper is to select an optimal route for the transportation of fertilizer in A agricultural company. Under the premise of traversing all the transportation nodes, the transportation route is shortest and the full load rate is the highest.In this paper, the improved savings algorithm and the improved ant colony algorithm are used to solve the problem, and the results obtained by the two algorithms are compared and analyzed. Finally, the result of the improved ant colony algorithm not only saves the mileage and the number of vehicles, but also increases the full load rate of the vehicle.First of all, this paper introduces the relevant theoretical knowledge, including logistics theory, the concept of agricultural material logistics, development status and improvement measures, as well as the concept of vehicle routing problems, elements, mathematical models, and so on.Secondly, this paper analyzes the common methods of logistics route selection and optimization, including precise algorithm, heuristic algorithm and intelligent algorithm, and explains in detail the basic principle, advantages and disadvantages, and adaptive range of the specific algorithm.This paper mainly introduces the saving algorithm, the improved saving algorithm, the ant colony algorithm and the improved ant colony algorithm.Thirdly, based on the improved saving algorithm and the improved ant colony algorithm, the results of the improved ant colony algorithm are compared and analyzed, and the result shows that the improved ant colony algorithm has more advantages in solving the vehicle route optimization problem.Finally, the paper summarizes the full text and analyzes the shortcomings of this paper.The dynamic nature of the vehicle routing problem makes the problem more complex, but still has a high research value.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U492.22;F426.72

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