Joint Optimization of Order Allocation and Rack Selection in the “Parts-to-Picker” Picking System Considering Multiple Stations Workload Balance

E-commerce companies generate massive orders daily, and efficiently fulfilling them is a critical challenge. In the “parts-to-picker” order fulfillment system, the joint optimization of order allocation and rack selection is a crucial problem. Previous research has primarily focused on these two aspects separately and has yet to consider the issue of workload balancing across multiple picking stations, which can significantly impact picking efficiency. Therefore, this paper studies a joint optimization problem of order allocation and rack selection for a “parts-to-picker” order picking system with multiple picking stations to improve order picking efficiency and avoid uneven workload distribution. An integer programming model of order allocation and rack selection joint optimization is formulated to minimize the racks’ total moving distance and to balance the orders allocated to each picking station. The problem is decomposed into three sub-problems: order batching, batch allocation, and rack selection, and an improved simulated annealing (SA) algorithm is designed to solve the problem. Two workload comparing operators and two random operators are developed and introduced to the SA iterations. Random instances of different scales are generated for experiments. The algorithm solutions are compared with those generated by solving the IP model directly in a commercial solver, CPLEX, and applying the first-come-first-serve strategy (FCFS), respectively. The numerical results show that the proposed algorithm can generate order allocation and rack selection solutions much more efficiently, where the moving distances of the racks are effectively reduced and the workloads are balanced among the picking stations simultaneously. The model and algorithm proposed in this paper can provide a scientific decision-making basis for e-commerce companies to improve their picking efficiency.