Research on Community Logistics Distribution Optimization Based on BDS Technology and Intelligent Algorithms

Abstract

With the growth of community populations and the diversification of consumer demands, community logistics distribution faces severe challenges in terms of timeliness, cost, and service quality. To address these issues, this paper proposes an optimized community logistics distribution model that integrates BeiDou satellite navigation technology and intelligent algorithms. By incorporating BDS's high-precision positioning and real-time traffic information, the model establishes a genetic algorithm (GA)-based route optimization model, a long short-term memory (LSTM)-based demand forecasting model, and a multi-agent reinforcement learning (MARL)-based real-time collaborative scheduling algorithm. This enables dynamic route planning, accurate demand prediction, and efficient resource allocation. Simulation experiments demonstrate that the proposed model reduces average delivery time by 18.7%, controls demand prediction errors within 8%, and significantly improves distribution efficiency and system responsiveness. This study provides theoretical support and technical pathways for the intelligent, green, and sustainable development of community logistics distribution.