基于RFID的配送中心拣货路径优化算法的研究
发布时间：2019-04-11 08:03
【摘要】：随着近几年在中国电子商务的快速发展，，一些B2C企业开始也建自己的物流配送中心和配送体系，配送中心是整个物流系统中的重要环节，配送中心的工作效率直接影响物流配送的快慢程度。物流配送服务水平的高低也直接影响客户的购物体验和服务满意度，这也间接的反映客户对公司的品牌信任度，所以如何有效的提高配送中心的管理和运作效率，己经成为众多B2C企业面临的最棘手的问题。 配送中心内部作业中包括多个环节，如：接货、搬运、存储、拣选、分拣、出货等。这些作业中拣选作业是非常重要的一个环节，在配送中心内部作业中占的比重较大，也是最耗时间的工作，因此可以说拣选效率直接影响配送中的整体运作效率和客户服务满意度。本文以双区型仓库为研究对象在配送中心货物管理系统中引入RFID技术，重点针对拣货车辆拣选路径进行了优化处理，以单个拣货车辆和多个拣货车辆的条件下各自单独的建立拣选路径问题的数学模型，设计相应的算法对拣货车辆行走路径进行了优化处理，从而有效的减少了拣货车辆拣货过程中的行走距离。 通过对拣选路径优化算法验证，证明单车辆拣选路径优化中混合遗传退火算法相比遗传算法的有效性，针对多车辆拣选路径优化中先用单车辆拣选路径优化的混合遗传算法对数学模型整体优化，就是先对订单进行分批处理，然后对每辆车的拣选路径用遗传算法求解TSP问题，这样可以在多车辆拣选情况下最大限度的减少拣选过程中的行走距离。 在编程求解方面，本文运用MATLAB语言进行编程实现问题的相关算法，对拣选路径优化模型进行求解，并在MATLAB R2010a平台上进行了算法的仿真实现。
[Abstract]:With the rapid development of e-commerce in China in recent years, some B2C enterprises have started to build their own logistics distribution center and distribution system. Distribution center is an important link in the whole logistics system. The efficiency of distribution center directly affects the speed and speed of logistics distribution. The level of logistics distribution service also directly affects the customer's shopping experience and service satisfaction, which also indirectly reflects the customer's brand trust in the company, so how to effectively improve the management and operational efficiency of the distribution center, It has become the most difficult problem faced by many B2C enterprises. Distribution center internal operations include a number of links, such as: pick-up, handling, storage, picking, sorting, shipping, and so on. Selection is a very important link in these operations, which accounts for a large proportion of the internal operations in the distribution center and is also the most time-consuming work. Therefore, it can be said that picking efficiency directly affects the overall operational efficiency and customer service satisfaction in distribution. In this paper, RFID technology is introduced into the cargo management system of distribution center with dual-area warehouse as the research object, and the optimization of picking path of picking vehicle is focused on. Under the condition of single picking vehicle and multiple picking vehicle, the mathematical model of picking path problem is established separately, and the corresponding algorithm is designed to optimize the walking path of picking vehicle, and the mathematical model of picking path problem is established separately under the condition of single picking vehicle and multiple picking vehicle. Thus effectively reduces the picking vehicle in the picking process of the walking distance. It is proved that the hybrid genetic annealing algorithm is more effective than the genetic algorithm in single vehicle picking path optimization through the verification of the picking path optimization algorithm. In the multi-vehicle picking path optimization, a hybrid genetic algorithm based on single vehicle picking path optimization is used to optimize the whole mathematical model, that is to say, the order is processed in batches first, and then the TSP problem is solved by genetic algorithm for the picking path of each vehicle. This can minimize the walking distance during the picking process in the case of multi-vehicle picking. In the aspect of programming solution, this paper uses MATLAB language to carry on the related algorithm of the programming realization problem, to solve the picking path optimization model, and has carried on the simulation realization to the algorithm on the MATLAB R2010a platform.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP18;TP391.44
本文编号：2456236
[Abstract]:With the rapid development of e-commerce in China in recent years, some B2C enterprises have started to build their own logistics distribution center and distribution system. Distribution center is an important link in the whole logistics system. The efficiency of distribution center directly affects the speed and speed of logistics distribution. The level of logistics distribution service also directly affects the customer's shopping experience and service satisfaction, which also indirectly reflects the customer's brand trust in the company, so how to effectively improve the management and operational efficiency of the distribution center, It has become the most difficult problem faced by many B2C enterprises. Distribution center internal operations include a number of links, such as: pick-up, handling, storage, picking, sorting, shipping, and so on. Selection is a very important link in these operations, which accounts for a large proportion of the internal operations in the distribution center and is also the most time-consuming work. Therefore, it can be said that picking efficiency directly affects the overall operational efficiency and customer service satisfaction in distribution. In this paper, RFID technology is introduced into the cargo management system of distribution center with dual-area warehouse as the research object, and the optimization of picking path of picking vehicle is focused on. Under the condition of single picking vehicle and multiple picking vehicle, the mathematical model of picking path problem is established separately, and the corresponding algorithm is designed to optimize the walking path of picking vehicle, and the mathematical model of picking path problem is established separately under the condition of single picking vehicle and multiple picking vehicle. Thus effectively reduces the picking vehicle in the picking process of the walking distance. It is proved that the hybrid genetic annealing algorithm is more effective than the genetic algorithm in single vehicle picking path optimization through the verification of the picking path optimization algorithm. In the multi-vehicle picking path optimization, a hybrid genetic algorithm based on single vehicle picking path optimization is used to optimize the whole mathematical model, that is to say, the order is processed in batches first, and then the TSP problem is solved by genetic algorithm for the picking path of each vehicle. This can minimize the walking distance during the picking process in the case of multi-vehicle picking. In the aspect of programming solution, this paper uses MATLAB language to carry on the related algorithm of the programming realization problem, to solve the picking path optimization model, and has carried on the simulation realization to the algorithm on the MATLAB R2010a platform.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP18;TP391.44
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