武汉JSY公司的配载与车辆路径集成优化研究

发布时间：2018-01-22 05:18

本文关键词： 配送车辆路径车辆装载集成优化改进遗传算法　出处：《西南交通大学》2015年硕士论文　论文类型：学位论文

【摘要】：随着生鲜产品电子商务的迅速发展,高昂配送成本严重阻碍了生鲜电子商务的发展。车辆路径优化问题及配载问题作为物流配送优化的重要组成,国内外学者已做了大量研究,以此改善配送效率和降低配送成本。本文充分利用已有研究成果,分析车辆路径和配载之间的关系,通过集成优化研究,实现配送方案的最大程度优化。本文的主要研究工作包括构建集成优化模型、设计改进遗传算法,JSY公司的实际算例分析。首先,本文介绍了生鲜电子商务的发展现状,以及存在的主要问题,概述了国内外在车辆路径及车辆装载方面单一优化及组合优化的研究成果,选取了生鲜电商领域极具创新性和代表性的JSY公司为研究对象,根据现实中货物配载及车辆路径问题的特征,建立了包含二维配载优化及非满载车辆路径优化的多目标集成优化模型,通过对模型的处理,运用权重系数变换法及归一化处理将多目标优化转换为单目标优化,使模型更加简化,求解也更加方便。其次,在算法设计上,根据模型特征对比分析了精确算法和启发式算法的优势和不足,根据适用性和计算效果选择了启发式算法中的遗传算法,为了让计算更加准确和高效,同时满足解决具体优化问题的需要,对基本遗传算法存在的不足进行了改进,采用了自然数编码及自适应交叉和自适应变异的改进遗传算法,并通过编写Matlab软件的程序文件及调用遗传算法工具箱实现算法设计。最后,利用集成优化模型和改进遗传算法优化JSY公司的部分配送任务,分析优化结果,验证了集成优化模型及改进遗传算法对于配送效率和成本有很好的优化效果。
[Abstract]:With the rapid development of electronic commerce of fresh products, high distribution cost seriously hinders the development of fresh electronic commerce. Vehicle routing optimization and stowage are important components of logistics distribution optimization. Scholars at home and abroad have done a lot of research to improve the efficiency of distribution and reduce the cost of distribution. This paper makes full use of existing research results to analyze the relationship between vehicle routing and stowage. The main work of this paper is to build an integrated optimization model and design a practical example of improved genetic algorithm (JSY). First of all. This paper introduces the development of fresh electronic commerce and the main problems, and summarizes the research results of single optimization and combination optimization in vehicle routing and vehicle loading at home and abroad. JSY Company, which is very innovative and representative in the field of fresh electricity business, is selected as the research object, according to the characteristics of the real cargo stowage and vehicle routing problem. A multi-objective integrated optimization model including two-dimensional stowage optimization and non-full load vehicle routing optimization is established. The method of weight coefficient transformation and normalization are used to transform the multi-objective optimization into single-objective optimization, which makes the model more simplified and the solution more convenient. Secondly, the algorithm design. According to the characteristics of the model, the advantages and disadvantages of the exact algorithm and the heuristic algorithm are compared and analyzed. According to the applicability and calculation effect, genetic algorithm is selected in the heuristic algorithm, in order to make the calculation more accurate and efficient. At the same time, to meet the needs of solving the specific optimization problem, the basic genetic algorithm is improved, using natural number coding and adaptive crossover and adaptive mutation improved genetic algorithm. And by writing the program file of Matlab software and calling genetic algorithm toolbox to realize the algorithm design. Finally, the integrated optimization model and improved genetic algorithm are used to optimize some of the distribution tasks of JSY Company. The results show that the integrated optimization model and the improved genetic algorithm have a good effect on distribution efficiency and cost.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U116.2;F252

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