配送中心订单分批问题研究

发布时间：2018-06-10 17:19

本文选题：订单分批 + 路径选择　；参考：《山东大学》2015年硕士论文

【摘要】：拣选作业是物流中心主要的作业流程之一,拣选作业成本占据着物流中心运营成本的60%,提高拣选效率是降低物流中心拣选成本的有效途径之一。在拣选客户订单之前,合理地对订单进行分批,将同一批的订单在一次拣选过程中同时完成,可以显著地节省拣选时间,提高拣选货物的效率,即使减少相当少的拣选时间也能相应的减少物流中心拣货成本。本文旨在研究一种实用有效方法,通过对订单进行合理的分批,使得完成所有订单货物拣选的总行走距离最小。首先,本文在总结订单分批常用算法的基础上,从路径选择的角度考虑建模,即先为每一个订单从一个路径集中选择一条路径,再将相同路径下的所有订单进行合并分批。模型以全部订单总行走距离最小为目标函数。其次,针对订单总行走距离最短问题提出了遗传算法(GA)和局部搜索算法(ILS)混合求解算法。模型的求解过程分为两个步骤：一是建立订单路径集；二是结合遗传算法(GA)和局部搜索算法(ILS)对订单进行分批合并。其中,遗传算法是用来为每一个订单选择一条最优路径；局部迭代搜索算法是用来对同一路径下的所有订单进行分批,目的是使分批数目最少。最后,在考虑了不同的拣选设备容量和订单数目的基础上,设计了8个算例实验。从计算效率、目标函数优化程度的角度分别对比三种算法,即本文提出的混合遗传算法和局部迭代搜索算法(GA-ILS)、先进先出算法(FCFS)和简单遗传算法(base-GA)。实验结果表明,本文提出的混合遗传算法和局部迭代搜索算法(GA-ILS)相比于FCFS和base-GA算法在总拣选距离上具有明显优势,在计算速度上比base-GA算法更快。
[Abstract]:Picking operation is one of the main processes of logistics center. Picking operation cost occupies 60% of the operating cost of logistics center. Improving picking efficiency is one of the effective ways to reduce the selection cost of logistics center. Before selecting the customer's order, the order can be divided reasonably, and the same batch of order can be completed at the same time in one picking process, which can significantly save the picking time and improve the efficiency of picking the goods. Even a relatively small reduction in picking time can reduce the cost of picking goods in logistics centers. The purpose of this paper is to study a practical and effective method to minimize the total walking distance to complete the picking of all the goods ordered by a reasonable order in batches. Firstly, on the basis of summarizing the common algorithms of order batching, this paper considers the modeling from the point of view of path selection, that is to say, we first select a path from one path set for each order, and then merge all orders under the same path into batches. The model takes the minimum total walking distance of all orders as the objective function. Secondly, a hybrid algorithm of genetic algorithm (GA) and local search algorithm (ILS) is proposed to solve the shortest total walking distance of orders. The solution of the model is divided into two steps: one is to establish the order path set, the other is to combine the genetic algorithm (GA) with the local search algorithm (ILS) to merge the orders in batches. The genetic algorithm is used to select an optimal path for each order and the local iterative search algorithm is used to batch all orders on the same path in order to minimize the number of batches. Finally, on the basis of considering the capacity of different sorting equipment and the number of orders, eight numerical examples are designed. From the point of view of computational efficiency and degree of optimization of objective function, three algorithms are compared, that is, hybrid genetic algorithm and local iterative search algorithm, first-in-first-out algorithm (FCFS) and simple genetic algorithm (GA-ILSU) and simple genetic algorithm (GA). The experimental results show that the proposed hybrid genetic algorithm and local iterative search algorithm (GA-ILS) have obvious advantages over FCFS and base-GA in total picking distance, and the computational speed is faster than that of base-GA.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F252.1

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