Model-based Optimization of Pod Point Matching Decision in Robotic Mobile Fulfillment System

This research is proposed to optimize the pod point matching decision in robotic mobile fulfillment system (RMFS). In an RMFS, pod point matching decision is to solve the problem of which storage point should be chosen as the target for a pod after its picking task. In this paper, two different perspectives are proposed to solve this problem: arrange one pod at a time and arrange multiple pods at a time. From both perspectives, the optimization model is developed using a concept of estimation for future travelling distance of pod. To solve these models, three-class-based strategy and Kuhn-Munkras (KM) algorithm are used. Simulation experiments are carried out to demonstrate the effectiveness of the model and algorithm. By comparing with other traditional strategies, it's found that using KM algorithm to solve the model of arranging multiple pods simultaneously will lead to good results.