基于自适应遗传算法的W公司仓库货位分配与优化研究

发布时间：2018-03-29 01:28

本文选题：遗传算法　切入点：多目标优化　出处：《华南理工大学》2015年硕士论文

【摘要】：仓储是商品流通的重要环节之一,也是物流活动的重要支柱。为满足一定时间内社会生产和消费的需要,必须储存一定量的物资,保证社会再生产过程的顺利进行。我国当前的仓储业正处在从传统仓储业向现代仓储业的过度阶段,随着土地的增值、人工成本的大幅上升和现代物流对仓储作业效率的需求,仓储作业必然要求提高空间的利用率、降低人工成本、提升响应速度和作业精准。本文以W公司LCM模组成品仓当前的仓储管理及货位分配为背景,探讨如何利用人工智能算法—遗传算法进行立体仓库和平面仓库混合存储的研究。针对仓储管理中存在的问题,在遗传算法理论研究和仓库作业流程详尽分析的基础上,重点研究了平面仓库与立体仓库混合存储的货位分配和优化问题。采用随机存储的动态货位分配策略,以考虑周转率的出入库效率、货架的稳定性、同类相邻存储以及产品的先进先出为优化目标,建立了平面仓库与立体仓库混合存储的多目标货位分配优化模型。为了简化计算和提高遗传算法的效率,采用了改进的遗传算子,运用权重系数变化法将多目标优化问题进行转换。对于散货的出库,运用运筹学中的整数规划思想来减少叉车往返的次数。最后,通过获取仓库产品的相关数据信息,设置货位优化模型的基本参数,利用Matlab仿真软件进行求解,从输出的货位坐标和立体仿真图形可以看出,本文所采用的自适应遗传算法能够使多目标货位分配数学模型有效地收敛到最优解,立体仿真图形清晰直观地展示了出入库货位分配与优化效果。在文章最后进行了课题研究工作总结,并指出后期的研究内容。
[Abstract]:Warehousing is one of the important links in the circulation of goods and also an important pillar of logistics activities. In order to meet the needs of social production and consumption within a certain period of time, a certain amount of materials must be stored. The current warehousing industry in our country is in the transitional stage from traditional warehousing industry to modern warehousing industry. With the increase of land value, the substantial increase of labor cost and the demand of modern logistics for the efficiency of warehousing operations, China's current warehousing industry is in a transitional stage of transition from the traditional warehousing industry to the modern warehousing industry. Warehouse operation must improve space utilization, reduce labor cost, improve response speed and precision. This paper takes the current warehouse management and location allocation of LCM module finished product warehouse of W Company as the background. This paper discusses how to use artificial intelligence algorithm-genetic algorithm to study the hybrid storage of stereoscopic warehouse and plane warehouse, aiming at the problems existing in warehouse management, based on the research of genetic algorithm theory and the detailed analysis of warehouse operation flow. This paper focuses on the allocation and optimization of cargo space in the mixed storage of plane warehouse and stereoscopic warehouse. The dynamic location allocation strategy of random storage is adopted to consider the efficiency of the turnover rate and the shelf stability. In order to simplify the calculation and improve the efficiency of genetic algorithm, a multi-objective cargo allocation optimization model for the mixed storage of plane warehouse and stereoscopic warehouse is established for the same adjacent storage and product first-in-first-out (FIFO). An improved genetic operator is used to transform the multi-objective optimization problem by using the weight coefficient variation method. For bulk goods, the integer programming idea in operations research is used to reduce the number of forklift commutations. By obtaining the relevant data information of warehouse products, setting up the basic parameters of the cargo location optimization model, and using the Matlab simulation software to solve the problem, we can see from the output coordinates and three-dimensional simulation graphics. The adaptive genetic algorithm used in this paper can effectively converge to the optimal solution of the multi-objective location assignment mathematical model. The three-dimensional simulation graphics show clearly and intuitively the effect of allocation and optimization of incoming and outgoing storage spaces. At the end of this paper, the research work is summarized and the later research contents are pointed out.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F274;TP18

【引证文献】