仓储作业动态货位优化技术研究与应用

发布时间：2018-04-25 10:36

本文选题：动态货位优化 + 电子标签拣选库　；参考：《东华大学》2017年硕士论文

【摘要】：随着现代社会经济的发展,科学技术、电子商务、智能化技术、仓储管理及作业智能化设备也相继引进,智能仓储业务应运而生。仓储作业是指从商品入库到商品发送出库的整个过程,可归纳为在库管理、出入库作业等。而在整个过程中拣货作业所占用的成本最高、耗时最多,其效率与准确性都对公司的服务品质有极大的影响。实现仓储作业中的动态货位优化是提升拣选作业效率的有效手段。电子标签拣选系统具有弹性控制操作时间和实时控制功能,自动化仓库系统具有省力,快速、准确操作、提高效率、降低物流仓储成本等优点。因此电子标签拣选系统和自动化立体库系统应用越来越广泛。但是现代仓储管理对信息化、服务综合化、自动化、智能化提出越来越高的要求,特别是在出入库作业的动态优化;优化模型对不同库型的适应;与企业现有ERP系统数据的集成,实现资源的优化配置等方面。因此现有的仓储管理与控制系统在动态货位优化的实现、可集成性以及通用性等方面仍需进一步研究与开发。本文紧密结合现代仓储管理的发展,主要面向自动化立体库与电子标签拣选库开展了动态货位优化技术研究。主要研究内容如下:(1)基于智能计算的动态货位优化数学模型在批量订单的情况下,为了提高自动化立体仓库(AS/RS)的整体作业效率,提出了以堆垛机作业均衡与高仓储效率为目标的立体库货位优化模型;为了提高电子标签拣选库(PTL)的效率,提出了以订单完成度、货位占有率以及货位聚集度为目标的拣选优化模型。(2)对动态货位优化数学模型中的权重因子决策由于本文基于电子标签拣选库(PTL)与自动化立体库(AS/RS)设计的动态货位优化数学模型中有多重准则因素,为了确定各准则的重要性引入层次分析法中的判断矩阵法,构建多种标度的判断矩阵并求解特征值,通过一致性检验后,通过实例测试比较决策出最佳权重因子。(3)动态货位优化算法设计及试验设计了二进制粒子群算法(Binary Particle Swarm Optimization,BPSO)和遗传算法(Genetic Algorithm,GA)对动态优化模型求解。并且通过实例测试比较分析了两种优化算法的性能。通过电子标签拣选库出库作业实例,验证了所提模型及算法的有效性,通过比较可得出BPSO比GA可提升8.5%的作业效率。(4)动态货位优化服务组件的实现针对基于Web的智能化仓储作业系统,利用面向服务的方法,将二进制粒群算法以及遗传算法封装为支持智能化仓储作业的服务组件,集成到仓储作业系统。系统针对电子标签拣选库(PTL)与自动化立体库(AS/RS)实现了智能分配订单作业及货位优化,满足了灵活应对实际拣选作业的需求。
[Abstract]:With the development of modern society and economy, science and technology, electronic commerce, intelligent technology, warehouse management and intelligent operation equipment have been introduced, and intelligent storage business has emerged as the times require. Warehouse operation refers to the whole process from goods entering to sending out of warehouse, which can be summed up as management in warehouse, operation in and out of storehouse and so on. The picking process occupies the highest cost and time consuming in the whole process, and its efficiency and accuracy have a great impact on the service quality of the company. It is an effective means to improve the efficiency of picking operation to realize dynamic cargo location optimization in warehousing. The electronic label picking system has the function of flexible control of operation time and real-time control. The automatic warehouse system has the advantages of saving labor, fast and accurate operation, improving efficiency and reducing the cost of logistics storage, etc. Therefore, the electronic label picking system and the automated stereo library system are more and more widely used. However, modern warehouse management has higher and higher requirements for information, service integration, automation and intelligence, especially for dynamic optimization of incoming and outgoing operations; The integration with the existing ERP system data, the realization of the optimal allocation of resources and so on. Therefore, the implementation of dynamic cargo location optimization, integration and versatility of the existing warehouse management and control system need to be further studied and developed. Based on the development of modern warehouse management, this paper focuses on the research of dynamic cargo location optimization for automated warehouse and electronic label picking warehouse. The main research contents are as follows: (1) in order to improve the overall operation efficiency of ASR / RS, the dynamic location optimization mathematical model based on intelligent calculation is used to improve the operation efficiency of ASR / RS in the case of bulk orders. In order to improve the efficiency of electronic label sorting library (PTL), the paper puts forward an optimized model of warehouse location with the aim of balancing the work of stacker and high storage efficiency, and puts forward the degree of order completion in order to improve the efficiency of the electronic label sorting library (PTL). The weight factor decision of the dynamic location optimization mathematical model is based on the electronic label picking library PTL) and the automatic stereoscopic warehouse ASP / RS) design dynamic goods based on the selection optimization model of cargo space occupancy and the degree of cargo location aggregation. (2) the decision of weight factor in the dynamic location optimization mathematical model is based on the electronic label picking library (PTL) and the automatic stereoscopic warehouse (ASP / RS). There are many criterion factors in the mathematical model of bit optimization. In order to determine the importance of each criterion, the judgment matrix method of AHP is introduced, and the judgment matrix of various scales is constructed and the eigenvalues are solved. The dynamic cargo location optimization algorithm is designed and tested. Binary Particle Swarm optimization algorithm (BPSO) and genetic algorithm (GA) are designed to solve the dynamic optimization model. The performance of the two optimization algorithms is compared and analyzed by example test. The validity of the proposed model and algorithm is verified by an example of tag picking library out of library. By comparison, it can be concluded that BPSO can improve the working efficiency by 8.5% than GA. The realization of dynamic cargo location optimization service component is aimed at the intelligent warehouse operation system based on Web, and the service-oriented method is used. Binary particle swarm algorithm and genetic algorithm are encapsulated as service components to support intelligent warehousing and integrated into warehouse job system. Aiming at tag picking library PTL) and automatic stereoscopic library ASP / RS), the system realizes the intelligent distribution of order operation and the optimization of cargo location, which meets the demand of flexible response to the actual picking operation.
【学位授予单位】：东华大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274;TP18

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