时变路网车辆路径问题研究综述OA

Time-dependent vehicle routing problem(TDVRP)is a key challenge in urban efficient distribution and logis-tics scheduling within the context of intelligent transportation.This paper systematically summarizes the TDVRP from aspects such as problem modeling,solution algorithms,and application scenarios.A classification framework for TDVRP is established,with an in-depth analysis of key variants,including those with time windows,capacity constraints,electric vehicles,and drone coordination.The technical features and development trends of time-dependent road network data sources are systematically reviewed,identifying current research hotspots.Furthermore,the evolution of solution algo-rithms is examined,spanning exact algorithms,heuristic algorithms,metaheuristic algorithms,and artificial intelligence methods,and a framework for algorithm performance evaluation and selection strategies is built.Research findings:TDVRP is evolving towards multi-objective and multi-constraint optimization,adaptive and scalable algorithms,and green and low-carbon directions.It is deeply integrated with cutting-edge technologies such as deep learning and rein-forcement learning,and its application scenarios are becoming increasingly diversified.