The clustered storage strategy can significantly improve the picking efficiency of the "parts-to-picker" order picking system, which has been widely used. The parallel warehousing strategy of multiple stock keeping units (SKU) can reduce the number of times of replenishment in and out of the same storage unit and save the system operation time. For the parallel replenishment optimization problem of the "parts-to-picker" order picking system under the clustered storage strategy, we established a linear programming model, calculated the closeness centrality of SKUs and the correlation between SKUs, and proposed a greedy algorithm based on SKU centrality and correlation (GASCC) and a simulated annealing algorithm based on SKU centrality and correlation (SASCC). Through comparison experiments, we concluded that the linear programming model can solve the small-scale problem. GASCC and SASCC have the effectiveness in solving the large-scale problem, where GASCC solves fast and SASCC can obtain approximate optimal solutions, which are better than other algorithms. For problems of different sizes, SASCC can optimize solutions by 5% to 25% compared to Random, GASCC, the simulated annealing algorithm (SA), genetic algorithm (GA), and particle swarm optimization (PSO). In addition, we analyzed the influence of algorithm parameters and system parameters on the optimization effect of the algorithm and the smaller the number of SKU types stored in a single storage unit, and the higher the number of buffer locations, the more obvious the optimization effect is. Finally, the comparison with other traditional metaheuristic algorithms proves the effectiveness of the proposed GASCC and SASCC.
Citation: Xin Wang, Yaohua Wu. Optimization of a parallel replenishment input sequence for a clustered storage 'parts-to-picker' order picking system[J]. Journal of Industrial and Management Optimization, 2026, 22(1): 310-335. doi: 10.3934/jimo.2026012
The clustered storage strategy can significantly improve the picking efficiency of the "parts-to-picker" order picking system, which has been widely used. The parallel warehousing strategy of multiple stock keeping units (SKU) can reduce the number of times of replenishment in and out of the same storage unit and save the system operation time. For the parallel replenishment optimization problem of the "parts-to-picker" order picking system under the clustered storage strategy, we established a linear programming model, calculated the closeness centrality of SKUs and the correlation between SKUs, and proposed a greedy algorithm based on SKU centrality and correlation (GASCC) and a simulated annealing algorithm based on SKU centrality and correlation (SASCC). Through comparison experiments, we concluded that the linear programming model can solve the small-scale problem. GASCC and SASCC have the effectiveness in solving the large-scale problem, where GASCC solves fast and SASCC can obtain approximate optimal solutions, which are better than other algorithms. For problems of different sizes, SASCC can optimize solutions by 5% to 25% compared to Random, GASCC, the simulated annealing algorithm (SA), genetic algorithm (GA), and particle swarm optimization (PSO). In addition, we analyzed the influence of algorithm parameters and system parameters on the optimization effect of the algorithm and the smaller the number of SKU types stored in a single storage unit, and the higher the number of buffer locations, the more obvious the optimization effect is. Finally, the comparison with other traditional metaheuristic algorithms proves the effectiveness of the proposed GASCC and SASCC.
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Xin Wang, Yaohua Wu. Optimization of a parallel replenishment input sequence for a clustered storage "parts-to-picker" order picking system[J]. Journal of Industrial and Management Optimization, 2026, 22(1): 310-335. doi: 10.3934/jimo.2026012
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