An Improved PSO Approach to the Joint Order Batching and Picker Manhattan Routing Problem
|關鍵字:||倉儲中心;揀貨;改良型粒子群優化法;order picking;improved particle swarm optimization;order batching|
In picking product items in a warehouse center to fulfill customer orders, the orders with high degrees of similarity, in practice, are categorized as the same batch to be picked, and then the routing of moving each batch of product items is planned so that repetitive traveling routes are avoided to shorten the total order picking distance. However, when the number of orders is huge and the types of product items are complicated, it is a challenging task to manually establish an overall order picking plan. As a result, this paper investigates how to efficiently solve the joint problem that integrates the order batching and the Manhattan picker routing, which aims to minimize the total order picking distance, while taking into account the capacity constraint of the picking car and the orders with multiple product items. We further propose an improved particle swarm optimization approach that additionally considers each particle’s previous bad experience to avoid bad solutions and increase the convergence efficiency, in which our solution representation can simultaneously handle the order batching combination as well as the picker routing. The idea behind our design is to transform the warehouse space into a grid, where virtual “order center” and “batch center” are defined. By calculating the distance between the two centers, similar orders are classified into the same batch. In addition, the theoretical analysis of convergence and stability of this approach is also derived. We predict that the proposed method can efficiently shorten the total order picking distance and further increase the picking efficiency.