This paper provides a comprehensive review of dynamic vehicle routing problems (DVRPs), which have gained renewed interest due to technological advancements. The authors classify DVRPs based on the quality and evolution of information, and introduce the concept of "degree of dynamism" to measure the level of change and urgency in customer requests. The paper discusses various applications of DVRPs, including services, goods transport, and person transport, and reviews solution methods such as periodic reoptimization, continuous reoptimization, stochastic modeling, and sampling approaches. It also highlights the importance of performance evaluation metrics like competitive analysis and the value of information. The paper concludes by noting the lack of a reference benchmark for DVRPs and suggests that adaptations of static routing benchmarks can be used for computational experiments.This paper provides a comprehensive review of dynamic vehicle routing problems (DVRPs), which have gained renewed interest due to technological advancements. The authors classify DVRPs based on the quality and evolution of information, and introduce the concept of "degree of dynamism" to measure the level of change and urgency in customer requests. The paper discusses various applications of DVRPs, including services, goods transport, and person transport, and reviews solution methods such as periodic reoptimization, continuous reoptimization, stochastic modeling, and sampling approaches. It also highlights the importance of performance evaluation metrics like competitive analysis and the value of information. The paper concludes by noting the lack of a reference benchmark for DVRPs and suggests that adaptations of static routing benchmarks can be used for computational experiments.