Optimization of Storage Allocation for a Robotic Mobile Fulfillment System Considering Multiple Workstations

This paper explores a storage allocation problem for the Robotic Mobile Fulfillment System (RMFS) considering the situation of multiple workstations. A mixed integer programming model is formulated as the joint optimization of storage allocation and mobile rack scheduling (SAMRS), to minimize the total distance AGV traveled. Then, an efficient meta-heuristic based on Adaptive Large Neighborhood Search and Greedy Algorithm (ALNS-GA) is proposed to solve the model. Based on the data from the distribution center of a publishing house in Shanghai, we conduct a case study to demonstrate the effectiveness of the algorithm. In small-scale cases, ALNS-GA is faster and more stable than GUROBI while ensuring optimality. In large-scale cases, ALNS-GA is far better than the two random methods.