articles

In the context of intensive growth in e-commerce volumes and an increase in the number of returns, the task of improving the efficiency of logistics management, including both direct and reverse flows, is becoming relevant. Reversible processes are particularly complex, due to the need to determine the real return reasons, which directly determine the choice of route nodes. The aim of the article is to develop a multi-model method for situational managing logistics flows in e-commerce, characterized by a comprehensive solution to the interrelated tasks of analysis of customer requests for returns and multi-criteria routing of material flows. To classify the return reasons, an intuitionistic fuzzy random forest was used, since it allows for the uncertainty, incompleteness, and inconsistency of customer data to be taken into account by using intuitionistic fuzzy sets and an ensemble of decision trees. To optimize routes in both forward and reverse logistics, the earthworm algorithm was used, thanks to a balanced approach to global and local search using two mechanisms of reproduction and Cauchy mutation. The proposed multi-model method is implemented as a software in the Python and integrated with corporate information systems and data warehouse. The results of his testing showed that the combined use of a text data processing tools, intuitionistic fuzzy random forest and an earthworm algorithm allows us to the development of an effective logistics management system. Customer request analysis and route optimization reduce operating costs for processing returns, while accumulated statistics on their causes provide the basis for proactively adjusting product policies and increasing the redemption rate.