基于大数据的仓储作业优化模型及算法设计

发布时间：2018-03-16 12:10

本文选题：仓储管理　切入点：大数据　出处：《兰州交通大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着社会不断发展,生产力不断进步,仓储管理越来越成为人们关注的焦点。作为一个独立的生产制造企业,为了更好的发展,必须摒弃陈旧的管理观念,积极引进新技术、新思想,改变仓储现状,最大限度的压缩货物在仓库的停留时间,从而达到节约成本,增加企业收益的效果。本文根据A公司仓储中心的实际情况,将现场调研和理论充分结合,运用大数据的观念建立“云仓储”的管理平台,并对货位优化和拣选路径优化建立数学模型,使用多种群遗传算法和遗传算法分别对各自优化模型进行求解,希望为企业的进一步优化决策提供建议。论文的主要内容包括以下几个方面:(1)分析了论文的研究背景和意义,分别从货位优化和拣货路径优化两个方面介绍了国内外研究现状以及亟待继续研究的问题和方向。以此为基础确定了本文研究主要内容和研究方法思路。(2)介绍了大数据、货位优化和拣货路径优化这三方面的相关理论,着重对遗传算法作了详细介绍。然后分析了A公司仓储中心管理现状,为后文解决实际问题建立优化模型以及算法设计提供理论基础和依据。(3)根据大数据的理论对仓储中心建立“云仓储”管理平台,对各项业务操作进行优化整合。然后建立多目标货位优化模型,并使用RS分析方法对指标赋权。最后根据多种群遗传算法步骤设计优化模型求解过程,并通过MATLAB软件仿真计算,得到更加合理的货位设置,同时验证了模型可靠性。(4)根据A公司仓储拣选作业实际情况及拣选路径优化目标建立了仓储中心拣选路径优化模型。利用S-shape策略法、最大间隙法和遗传算法对拣选路径问题进行分析和优化求解,并采用MATLAB软件对优化模型进行仿真设计,得到优化后的拣选作业行走距离。通过对三种优化方法优化后的行走路程进行对比分析,基本达到拣选路径优化的目标。
[Abstract]:With the continuous development of society and the continuous progress of productivity, warehouse management has become the focus of more and more attention. As an independent manufacturing enterprise, in order to develop better, it is necessary to abandon the old management concept and actively introduce new technology. New ideas, changing the status quo of storage, minimizing the stay time of goods in the warehouse, thus achieving the effect of saving costs and increasing the income of enterprises. Combining the field investigation with the theory, using big data's idea to establish the management platform of "cloud storage", and to establish the mathematical model of the cargo location optimization and the picking path optimization. Using multi-population genetic algorithm and genetic algorithm to solve their respective optimization models, we hope to provide suggestions for further optimization decision. The main contents of this paper include the following aspects: 1) analyze the research background and significance of the paper. This paper introduces the status quo of domestic and international research and the problems and directions for further research from two aspects of cargo location optimization and picking route optimization respectively. On this basis, the main contents and research methods of this paper are determined and big data is introduced. The related theories of cargo location optimization and picking route optimization are introduced in detail in this paper, and then the current situation of warehouse center management in Company A is analyzed. It provides the theoretical basis and basis for establishing optimization model and algorithm design for solving practical problems later.) based on big data's theory, the "cloud storage" management platform is established for the storage center. The multi-objective cargo location optimization model is established, and the index is weighted by RS analysis method. Finally, according to the steps of multi-population genetic algorithm, the solution process of the optimization model is designed, and the simulation calculation is carried out by MATLAB software. At the same time, the reliability of the model is verified. (4) based on the actual situation of warehouse picking operation and the selection path optimization goal of A company, a storage center picking path optimization model is established, and the S-shape strategy method is used. The maximum gap method and genetic algorithm are used to analyze and solve the picking path problem, and MATLAB software is used to simulate the optimization model. Through the comparison and analysis of the three optimization methods, the target of path selection optimization is basically achieved.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274;TP311.13;TP18

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