Order Picking Method for Multi-entity Cooperation in Picking Warehouses

Abstract

In recent years, different kinds of autonomous robots have been widely studied and applied in warehouses to meet the increasing demand for dealing with more customer orders, providing shorter delivery time, and achieving higher system scalability. As the core operation of the modern warehousing system, order picking is an area in which many researchers are trying to find good strategies to apply robots to improve the performance and efficiency of order picking. This dissertation provides a Multi-entity Cooperative Order Picking (MCOP) strategy in which two types of entities (pickers and transporters) collaborate in a picker-to-parts warehouse to complete order picking. We first define MCOP, and describe the components, the working pattern, and the operating data structure involved in MCOP. An animated, data-generated, and data-driven simulation model is developed to represent a visualized realization of the processes in MCOP and provide a testbed for performance evaluation under different configurable settings. Next, we develop a MILP model to find the optimal operational decisions about workloads and routes for all entities in MCOP. Then, to overcome the inefficiency of using the proposed model to deal with the situation that a large number of items need to be retrieved in a pick wave, we develop an alternative algorithm called Hetero-ACO to find the operational decisions in a short time. A two-stage structure is applied to search the operational decisions for pickers and transporters, respectively. Lastly, we explore different combinations of certain distributions of product picking times and entity traveling speeds to analyze the impact of the variabilities on the makespan of MCOP and the wait time of pickers. The finding of this work can potentially help warehouse owners find the improvement in upgrading manual order pickings with assistive robots and guide the warehouse owners in how to apply robots to assist order pickings.