快速交货下的电商模块化拣货策略研究

发布时间：2018-03-07 13:00

本文选题：订单拣货　切入点：模块化　出处：《华中科技大学》2016年硕士论文　论文类型：学位论文

【摘要】：电子商务的迅猛发展使得电商企业之间的竞争日益加剧。随着电商企业竞争的不断升级,如何提高订单的响应速度已经成为各企业关注的重要问题。此外,“双11”“双12”“周年庆”等促销活动的出现,导致拣货作业工作量稳定性下降,这使得企业的订单响应速度越来越慢及响应速度的稳定性越来越差。因此,如何提高订单的响应速度以及保证响应速度的稳定性是各电商企业关注的重要问题。针对以上问题,本文提出了仓库模块化拣货策略。即通过对企业以往订单的分析,找出不同订单之间大量重复出现的产品组合,并且通过一定的优化策略,找出其中有效的产品组合,将其定义为模块。本文首先对仓库拣货系统优化、模块化以及预测方法等概念以及相关研究现状进行了梳理,为本研究的深入奠定理论基础;其次,对本文研究的问题进行了详细描述,对整个拣货流程的各个环节进行了详细阐述,并建立了数学模型;再次,详述了为解决本文提出的问题而设计的相关算法;最后,本文利用现实数据模拟模块化拣货及模块预测的效果,结果证明模块化拣货可以有效地提高仓库拣货的效率,使用BP神经网络、Elman神经网络以及指数平滑的组合预测方法可以有效地预测模块数量,为后续的研究提出了新的研究思路和方向。本文针对电商企业存在的现实问题,提出了模块化拣货策略,为B2C电商仓库拣货问题的解决提供了新思路,对后续的相关研究有一定的参考价值。
[Abstract]:With the rapid development of electronic commerce, the competition between e-commerce enterprises is becoming more and more intense. How to improve the response speed of orders has become an important concern of enterprises. In addition, the appearance of sales promotion activities such as "Shuang11" and "Shuang12" and "anniversary" has resulted in a decline in the stability of the workload of picking operations. Therefore, how to improve the response speed of orders and how to ensure the stability of response speed is an important concern of e-commerce enterprises. In this paper, the modularized picking strategy of warehouse is put forward, that is, through the analysis of the past orders, we find out the product combination which appears repeatedly among different orders, and find out the effective product combination through certain optimization strategy. Firstly, this paper combs the concepts of warehouse picking system optimization, modularization and prediction methods and related research status, which lays a theoretical foundation for this research. Detailed description of the problems studied in this paper, the whole picking process of each link were described in detail, and established a mathematical model; thirdly, detailed design for solving the problems raised in this paper related algorithms; finally, This paper simulates the effect of modular picking and forecasting by using real data. The results show that modular picking can effectively improve the efficiency of picking goods in warehouse. Using BP neural network Elman neural network and exponential smoothing combined forecasting method can effectively predict the number of modules, and put forward a new research idea and direction for further research. The modular picking strategy is put forward, which provides a new way to solve the problem of picking goods in B2C e-commerce warehouse, and has certain reference value for the related research in the future.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F724.6;F274

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