A job assignment model for conveyor-aided picking system

Due to development and popularity of the information and automation technologies, 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. Traditionally, the job assignment of a conveyor system is conducted by means of empirical rules or arbitrary decision of the planner and the task is usually time-consuming. 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. On the other hand, based on the items denoted in the picking schedule, the candidate job assignment plans are generated randomly or via the exhaustion method. Afterwards, on the basis of the derived empirical indices and corresponding weights, the optimal assignment plan can be determined. In addition to the job assignment algorithm, this study establishes a job assignment system for the conveyor-aided picking system. A real-world case is also presented to verify performance of the proposed methodology. In summary, the job assignment model proposed in this study can significantly reduce the time required for job planning for the conveyor-aided picking system and enhance applicability of the job assignment plans.