A Novel Scattered Storage Policy Considering Commodity Classification and Correlation in Robotic Mobile Fulfillment Systems

The commodity storage assignment problem (CSAP), which assigns stock-keeping units (SKUs) to a suitable location for matching the customer demand patterns, is crucial for improving the order picking efficiency. In this study, we jointly consider the SKUs classification and correlation, and propose a new scattered storage policy named scattered-correlation storage policy based on the commodity classification (SCSPCC) for mitigating CSAP in the robotic mobile fulfillment systems (RMFS). We call the new problem CSAP-SCSPCC. To address this problem, we construct a mixed-integer programming model, and propose a novel variable neighborhood search with self-adaption and simulated annealing acceptance mechanisms (SA-VNSSA). Besides, a heuristic algorithm is proposed to select the minimum number of shelves to evaluate the optimization effect of SA-VNSSA and SCSPCC in terms of the number of shelf transports. Extensive numerical experiments are conducted on small-, medium-, and large-scale instances, respectively. The results reveal that the proposed model and algorithms are reasonable and effective in solving CSAP-SCSPCC compared with the state-of-the-art methods. Specifically, SA-VNSSA outperforms the three state-of-the-art comparison algorithms [i.e., SA-1 (Muppani and Adil, 2008), SA-Pop (Assadi and Bagheri, 2016), and SA-2 (Zhang et al., 2019)] by more than 4.19% and 3.23% on average in medium- and larger-scale instances, respectively. The comparisons between SCSPCC and CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019) show that the order picking efficiency is improved by our SCSPCC more than 6.31%. It is can be concluded that SCSPCC is efficient and robust to match the SKU storage pattern and customer demand patterns in e-commerce retail. Note to Practitioners—Robotic mobile fulfillment systems (RMFS) have been widely used in the warehouses of Amazon, Jingdong, Cainiao, and so on. Considering practical situations and requirements in commodity storage assignment problems (CSAP) is necessary for improving RMFS order picking efficiency. We proposed a new problem named CSAP-SCSPCC for RMFS. Particularly, SCSPCC is a novel scattered storage policy that can assign best-selling SKUs and general-selling SKUs to a suitable location based on the SKU correlation, respectively. Computational results with small-, medium-, and large-scale instances show that our proposed SA-VNSSA and SCSPCC are effective, robust, and practically applicable compared with two state-of-the-art approaches [i.e., CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019)]. Compared with CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019), SCSPCC can improve order picking efficiency by more than 6.31%. In summary, the methods proposed in our work can match the SKU storage mode and the customer demand patterns in a giant e-commerce retail warehouse. This research work can contribute to the improvement of RMFS picking efficiency. In the future, it is necessary to study multiple problems in RMFS jointly, including CSAP-SCSPCC, shelves storage assignment problems, and order batching, etc.