Sustainability 2026, 18(2), 743; https://doi.org/10.3390/su18020743 (registering DOI) - 11 Jan 2026
Abstract
Currently, with the widespread popularization of e-commerce systems, enterprises have increasingly high requirements for the timeliness of order fulfillment. It has become particularly critical to enhance the operational efficiency of heterogeneous robotic “goods-to-person” (G2P) systems in book e-commerce fulfillment, reduce enterprise operational costs,
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Currently, with the widespread popularization of e-commerce systems, enterprises have increasingly high requirements for the timeliness of order fulfillment. It has become particularly critical to enhance the operational efficiency of heterogeneous robotic “goods-to-person” (G2P) systems in book e-commerce fulfillment, reduce enterprise operational costs, and achieve highly efficient, low-carbon, and sustainable warehouse management. Therefore, this study focuses on determining the optimal storage location assignment strategy and order batching method. By comprehensively considering the characteristics of book e-commerce, such as small-batch, high-frequency orders and diverse SKU requirements, as well as existing system issues including uncoordinated storage assignment and order processing, and differences in the operational efficiency of heterogeneous robots, this study proposes a joint optimization framework for storage location assignment and order batching centered on a multi-objective model. The framework integrates the time costs of robot picking operations, SKU turnover rates, and inter-commodity correlations, introduces the STCSPBC storage strategy to optimize storage location assignment, and designs the SA-ANS algorithm to solve the storage assignment problem. Meanwhile, order batching optimization is based on dynamic inventory data, and the S-O Greedy algorithm is adopted to find solutions with lower picking costs. This achieves the joint optimization of storage location assignment and order batching, improves the system’s picking efficiency, reduces operational costs, and realizes green and sustainable management. Finally, validation via a spatiotemporal network model shows that the proposed joint optimization framework outperforms existing benchmark methods, achieving a 45.73% improvement in average order hit rate, a 48.79% reduction in total movement distance, a 46.59% decrease in operation time, and a 24.04% reduction in conflict frequency.
Full article
(This article belongs to the Section Sustainable Management)
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