自动化立体仓库拣货系统的建模与优化研究

发布时间：2018-01-11 23:02

本文关键词：自动化立体仓库拣货系统的建模与优化研究　出处：《青岛大学》2013年硕士论文　论文类型：学位论文

【摘要】：自动化立体仓库是一种采用高层立体货架储存物资,计算机系统进行控制管理,自动控制堆垛机及运输车进行存取作业的仓库,它作为仓储管理的重要组成部分,其工作效率直接影响着整个仓储作业的运作效率。在企业竞争日趋激烈的环境下,效率己被视为企业的竞争核心,这就对自动化仓储技术提出新的挑战。自动化立体仓库主要包括货物输送系统、拣货系统、管理与控制系统等部分,拣货系统是自动化立体仓库的关键部分,其流程中拣货作业时间占整个仓库运作时间的一半以上,因此,拣货作业的效率决定着自动化仓库的运作效率。影响拣货作业效率的关键因素是拣货路径的长短,这就需要寻找优化拣货路径的方法。本文针对如何提高拣货系统效率寻求方法。首先简述自动化立体仓库的发展和它的结构组成,描述拣货系统的流程,分析影响拣货效率的因素,得出优化拣货路径是提高拣货效率的关键。然后提出优化拣货路径的方法,重点研究遗传算法、蚁群算法和粒子群算法在寻求最优拣货路径的实现方法。最后文章提出一种基于启发式规则的算法,用此算法求解以拣货时间最小化为目标的数学模型,并在实例中与遗传算法比较,证明其实用价值。此算法简单并且运算时间短,在实际应用方面,可考虑用于堆垛机的单机自动运行功能中,由PLC编程实现。
[Abstract]:Automatic warehouse is a kind of warehouse which uses the storage material of the high level shelf, the computer system carries on the control management, automatically controls the stacker and the transport vehicle to carry on the access operation. As an important part of warehouse management, its working efficiency directly affects the operation efficiency of the whole warehouse operation. In the increasingly competitive environment, efficiency has been regarded as the core of enterprise competition. This poses a new challenge to automated warehousing technology. Automated warehouse mainly includes cargo delivery system, picking system, management and control system, etc. Picking system is the key part of automated three-dimensional warehouse. The picking time in the process accounts for more than half of the operation time of the whole warehouse, therefore. The efficiency of picking operations determines the operational efficiency of automated warehouses, and the key factor affecting the efficiency of picking operations is the length of the picking path, which needs to find a way to optimize the picking path. This paper aims at how to improve the efficiency of picking system. Firstly, the development and structure of automated warehouse are briefly described, the process of picking system is described, and the factors affecting the efficiency of picking are analyzed. It is concluded that optimizing the picking path is the key to improve the picking efficiency. Then the method of optimizing the picking path is put forward, and the genetic algorithm is studied emphatically. Ant colony algorithm (ACA) and particle swarm optimization (PSO) algorithm are used to realize the optimal picking path. Finally, an algorithm based on heuristic rules is proposed to solve the mathematical model with the aim of minimizing the picking time. Compared with the genetic algorithm, this algorithm is simple and has short operation time. In practical application, it can be considered to be used in the single machine automatic running function of stacker, which can be realized by PLC programming.
【学位授予单位】：青岛大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP18;TH692.3

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