中小型企业的仓储管理系统设计与优化

发布时间：2018-01-20 08:36

本文关键词： 仓储管理系统 Php+Mysql 遗传算法二次更新型粒子群算法　出处：《东华理工大学》2016年硕士论文　论文类型：学位论文

【摘要】：仓储管理系统(WMS)是仓储管理信息化的具体形式,是现代物流业的重要环节。调研发现,宁波大量中小型企业的仓储管理仍处于人工化状态,并没有应用仓储管理系统,这大大阻碍了宁波中小型企业的发展。本文以宁波A企业作为中小型企业的代表,通过调研A企业的仓储管理现状及生产需求,引用PHP脚本语言与Mysql服务器,设计出了一套适合中小型企业的仓储管理系统。系统包括“入库管理”、“出库管理”、“生产管理”、“库存管理”、“权限管理”五大功能模块。仓储管理系统在A企业运行后,跟进发现系统内的货位信息并没有得到充分利用,这降低了仓储管理系统的应用效果。因此针对仓库的货位分配,本文从出入库效率和货架稳定性两个方面建立了多目标货位优化模型,并使用遗传算法(GA)将货位优化方案以二进制编码的形式转换成染色体,经过一定代数的交叉变异后得到最优货位分配方案。在MATLAB平台上仿真后,验证了遗传算法在解决货位优化问题中的有效性。然而受到算法本身交叉变异特性的影响,遗传算法在优化过程中的收敛速度较慢。粒子群算法(PSO)通过粒子间的信息共享完成更新迭代,因此PSO即拥有收敛速度快的优势、又包含局部收敛的劣势。粒子群算法应用到离散型问题中主要用于解决旅行商问题(TSP),而用于解决货位优化问题的研究较少。本文通过引用换位减思想改变粒子群的更新规则,及混合具有概率突跳性的模拟退火算法(SA),提出了一种创新算法即二次更新型粒子群算法(TUPSO)。在MATLAB平台上仿真,验证了二次更新型粒子群算法在解决货位优化问题中的有效性。与遗传算法进行对比后,发现二次更新型粒子群算法较遗传算法在优化效果相近的结果下不仅收敛代数提前了83代、而且货位分配范围扩大了16个货位面积。
[Abstract]:Warehouse management system (WMS) is a specific form of warehouse management information, is an important link in the modern logistics industry. Research found that a large number of small and medium-sized enterprises in Ningbo storage management is still in a state of artificial. There is no application of warehouse management system, which greatly hinders the development of small and medium-sized enterprises in Ningbo. This paper takes Ningbo A enterprise as the representative of small and medium-sized enterprises, through investigating the status quo of storage management and production demand of A enterprise. Referring to PHP script language and Mysql server, a warehouse management system suitable for small and medium-sized enterprises is designed. The system includes "storage management", "outbound management" and "production management". "inventory management", "authority management" five functional modules. Warehouse management system in A enterprise operation, follow up found that the system of cargo location information has not been fully utilized. This reduces the application effect of the warehouse management system. Therefore, in view of the warehouse space allocation, this paper establishes a multi-objective cargo location optimization model from the two aspects of storage efficiency and shelf stability. The genetic algorithm (GA) is used to convert the optimization scheme into chromosomes in the form of binary coding. After a certain algebraic crossover mutation, the optimal allocation scheme is obtained. The simulation is done on the MATLAB platform. The validity of the genetic algorithm in solving the cargo location optimization problem is verified. However, the genetic algorithm is affected by the cross-mutation characteristics of the algorithm itself. The convergence speed of genetic algorithm is slow in the process of optimization. Particle Swarm Optimization (PSO) completes the update iteration by sharing information among particles, so PSO has the advantage of fast convergence speed. Particle swarm optimization (PSO) is mainly used to solve the traveling salesman problem (TSP). In this paper, we use the idea of transposition subtraction to change the updating rules of PSO and the simulated annealing algorithm with probabilistic jump (SAA). In this paper, an innovative algorithm, called quadratic and newer particle swarm optimization algorithm (TUPSOO), is proposed, which is simulated on the platform of MATLAB. The effectiveness of the new quadratic particle swarm optimization algorithm in solving the cargo location optimization problem is verified, and compared with the genetic algorithm. It is found that the new quadratic particle swarm optimization algorithm not only brings forward the convergence algebra 83 generations ahead of the genetic algorithm, but also expands the area of cargo location by 16 points compared with the genetic algorithm.
【学位授予单位】：东华理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP315

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