Abstract
The traditional logistics industry gradually implements the automation (e.g., sorters) or semi-automation (e.g., conveyors) techniques to support the picking operation in order to save huge labor cost of distribution centers (DCs). As for the semi-automatic picking system using the conveyor system, one of the critical issues is to appropriately assign the items to be picked to each workstation in the conveyor system in order to balance workload of each workstation and to enhance the overall resource utilization of a DC. As for the job assignment of the conveyor-aided picking system, this study develops a model to generate workload-balanced job assignment suggestions to the planner. In the proposed methodology, the expertise of job assignment for conveyor aided picking system is extracted via interviews in order to derive the empirical rules for conveyor job assignment. After that, the empirical rules are quantitatively converted into empirical indices and the weights of empirical indices can be determined via the historical job assignment records. This study also establishes a job assignment system for the conveyor aided picking system.