Integrated Optimization of Order Processing and Robot Scheduling in Parts-to-Picker System

摘要

In a parts-to-picker system, robots move racks from the storage area to picking stations where pickers pick products from the racks. This paper proposes an Order Processing and Robot Scheduling Problem (OPRSP), which optimizes order allocation, rack selection, and robot scheduling together. A mixed integer programming model is developed for OPRSP to minimize the system completion time of fulfilling a given set of customer orders. In OPRSP, customer orders are reorganized into multiple order batches, each allocated to a picking station and treated as an aggregated order. A multi-station visit policy is employed to allow one rack carried by a robot to visit multiple picking stations. A variable neighborhood search-based algorithm is proposed to solve OPRSP. Seven neighborhood structures are specifically devised for OPRSP. Some heuristic algorithms, including order batching algorithm, rack selection algorithm, order batch allocation algorithm, robot task assignment algorithm, and robot scheduling algorithm, are newly devised for initial solution generation and neighborhood structures. The superior performance of the proposed method is validated through a series of experiments and comparisons with other approaches. Experiments reveal that compared to optimizing robot scheduling only, integrating robot scheduling with order processing decisions can greatly improve picking efficiency.