Decision support system for managing returns in e-commerce based on the earthworm algorithm and fuzzy random forest

The article is devoted to the study of the possibilities of using intelligent data analysis methods and bioinspired optimization algorithms in the management of returns in e-commerce. The paper proposes the architecture of a decision support system (DSS) that combines an earthworm algorithm for multi-criteria optimization of return routes and a fuzzy random forest for classifying the causes of returns. It is shown that the use of bioheuristics makes it possible to form stable and balanced routes taking into account the distance, time and complexity of the logistics network, and the use of a fuzzy random forest provides an interpretable analysis of subjective and incomplete customer data. The proposed DSS architecture demonstrates high flexibility, scalability, and the ability to integrate with logistics platforms and WMS, providing comprehensive support for return flow analysis. The economic efficiency of the implementation of the system is considered, including reducing logistical costs, reducing the number of unjustified refunds and improving the quality of customer service.

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