Abstract
Robotic mobile fulfillment system (RMFS) is a new type of parts-to-picker order picking system, where robots carry inventory pods to stationary pickers. Because of the difference in working mode, traditional storage assignment methods are not suitable for this new kind of picking system. This paper studies the storage assignment optimization of RMFS, which is divided into products assignment stage and pods assignment stage. In the products assignment stage, a mathematical model maximizing the total correlation of products in the same pods is established to reduce the times of pod visits, and a scattered storage policy is adopted to reduce system congestion. A heuristic algorithm is designed to solve the model. In the pods assignment stage, a model is established minimizing the total picking distance of the mobile robots considering the turnover rate and the correlation of pods as well as the workload balance among picking corridors. A two-stage hybrid algorithm combining greedy algorithm and improved simulated annealing is designed to solve the model. Finally, a simulation experiment is carried out based on the historical order data of an e-commerce company. Results show that the storage assignment method proposed in the paper significantly improves the efficiency of order picking.
Highlights
E-commerce orders are generally large in quantity, small in batch, and unstable, so e-commerce order fulfillment can be quite challenging for warehouses
We study the joint optimization of products assignment and pods assignment of Robotic mobile fulfillment system (RMFS) considering the correlation, turnover rate of products and pods, as well as system congestion factors. e main ideas of optimization in each stage are discussed below
In order to adapt to the nature of the problem and improve the search efficiency, the simulated annealing algorithm is improved from three aspects: (1) a greedy algorithm based on pods correlation is used to generate the initial solution; (2) the principle of pods with high turnover rate placed near picking stations is used to generate the new solution; and (3) at the same time, the workload among corridors is balanced to reduce system congestion
Summary
E-commerce orders are generally large in quantity, small in batch, and unstable, so e-commerce order fulfillment can be quite challenging for warehouses. As an important optimization direction of RMFS, storage assignment has a direct impact on the total travel time/distance of robots, the efficiency of order picking.
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