An efficient storage strategy for retail e-commerce warehousing is important for minimizing the order retrieval time to improve the warehouse-output efficiency. In this paper, we consider a model and algorithm to solve the cargo location problem in a retail e-commerce warehouse. The problem is abstracted into storing cargo on three-dimensional shelves, and the mathematical model is built considering three objectives: efficiency, stability, and classification. An artificial swarm algorithm is designed to solve the proposed models. Computational experiments performed on a warehouse show that the proposed approach is effective at solving the cargo location assignment problem and is significant for the operation and organization of a retail e-commerce warehouse.

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The allocation issues of the location of the cargo have affected the operational efficiency of retail e-commerce warehouses tremendously. Adjusting the cargo location with the change of the order and the operation of the warehouse is a significant research area. A novel approach employing the FP-Tree and the Artificial Fish Swarm Algorithms is proposed. Firstly, energy consumption and shelf stability are employed for the location-allocation. Secondly, the association rules among product items are obtained by the FP-Tree Algorithm to mine frequent list of items. Furthermore, the frequency and the weight of product items are taken into account to ensure the local stability of the shelf during data mining. Thirdly, another method of the location-allocation is obtained with the objectives of the energy consumption and the overall shelf stability along with the frequent items stored nearby that is conducted by the Artificial Fish Swarm Algorithm. Finally, the picking order distance is obtained through two methods of the location-allocation above. The performance and efficiency of the novel introduced method have been confirmed by running the experiment. The outcomes of the simulation suggest that the introduced method has a higher performance concerning criterion called the picking order distance.

The secondary packaging and secondary transportation caused by products’ online return lead to a large amount of resource waste and environmental damage, which are not conducive to the green and sustainable development of enterprises. As consumers become more aware of environmental protection, their purchase and return behaviors will also change, prompting e-commerce platforms to adjust their return strategies. In this context, this paper aims to study the optimal return strategy that balances enterprises’ social benefits and environmental impact. The Stackelberg game models are constructed based on two behaviors: environmental protection publicity of e-commerce platforms and consumer return. The impacts of return strategies on the environment and the benefits of supply chain members are investigated. Results show that environmental protection publicity and return compensation can stimulate the expected sales volume. The optimal environmental protection publicity depends on the return rate. When the return rate is high, and the repurchase price is low, the optimal decision of the e-commerce platform is not to introduce return freight insurance so as to maintain its own benefits and reduce the environmental impact.

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