Optimization of Express Cabinet Logistics Network Layout Based on Genetic Algorithm by AI-driven

Abstract

The study of site selection problems has rapidly advanced with societal progress, evolving into a systematic science. This paper reviews existing theoretical methods for site selection optimization, noting their maturity and widespread application in modern society, and specifically references their relevance to the strategic placement of express cabinets. The optimal placement of express cabinets directly impacts customer satisfaction and logistics efficiency, while also considering urban planning and traffic conditions. This research leverages a Genetic Algorithm for the optimization, demonstrating how computational intelligence, a key facet of artificial intelligence, can effectively address complex real-world logistical challenges. The integration of AI-driven approaches, such as advanced metaheuristics, is crucial for developing robust and adaptive logistics networks that can respond to dynamic urban environments and evolving consumer demands.