Recent advances in commercial vehicle operations (CVO), especially in communication and information technologies, allow the study of dynamic vehicle routing problems under new and updated information, such as traffic conditions and new customers. Two major operational benefits of CVO include ( a) dynamically assigning vehicles to time-sensitive demands, and ( b) efficiently rerouting vehicles according to current traffic conditions. In this research, stochastic vehicle routing problems (SVRP) are considered and extended to incorporate real-time information for dynamic vehicle routing problems. The SVRP model is formulated by a chance-constrained model and is solved by CPLEX with branch-and-bound techniques. Numerical experiments are conducted in a Taichung city network to investigate dynamic vehicle routing strategies under real-time information supply strategies and to assess the effectiveness of such strategies in a dynamic perspective.
A real-time Decision Support System for Big Data Analytic: A case of Dynamic Vehicle Routing Problems
