Dynamic Vehicle Routing Optimization for Urban Distribution Under Real-Time Demand Fluctuations

With the rapid rise of e-commerce, the logistics and distribution industry is experiencing unprecedented growth. In particular, intra-city distribution is the crucial “last mile” of logistics and plays a decisive role in determining overall customer satisfaction. This study improves an inclusive vehicle routing optimization framework for intra-city distribution under dynamic demand. The initiative of a novel memetic algorithm that efficiently solves the NP-hard dynamic vehicle routing problem while guaranteeing high service quality and cost reduction. However, modern intercity distribution systems often struggle with low information, unpredictable demand patterns, and high operational costs due to scattered customer locations and dynamic order information. Addressing these challenges, this study suggests a comprehensive and intelligent vehicle routing optimization framework tailored for intracity distribution under dynamic demand conditions. The proposed system begins with a grey prediction model for short-term demand forecasting across many distribution regions, permitting differentiated vehicle loading methods to optimize transportation costs and improve operational effectiveness. Building upon this, a dynamic vehicle routing optimization model is formulated to reduce costs while assuring high levels of customer satisfaction within strict delivery time windows. To competently manage fluctuating demand, a dynamic information processing approach is introduced; prioritizing customer needs based on their urgency and importance, thereby guaranteeing the timely delivery of critical orders with minimal computational overhead. Moreover, a novel memetic algorithm is considered to solve the complex NP-hard dynamic vehicle routing problem. This algorithm integrates an adaptive elite genetic algorithm for global search with improved crossover and mutation operators, improved by local search methods such as 2-opt and swap methods to refine solutions. Numerical experiments validate the feasibility and performance of the proposed method, indicating significant improvements over conventional fully loaded vehicle schemes and regular route update methods. The results highlight the practical value of the system in attractive intra-city logistics efficiency, reducing costs, and inspiring customer service standards.