Efficient Intelligent Optimized Algorithm for Dynamic Vehicle Routing Problem

In order to solve the dynamic vehicle routing problem (DVRP) containing both dynamic network environment and real-time customer requests, an efficient intelligent optimized algorithm called IOA is proposed in this paper, which takes advantages of both global searching ability of evolutionary algorithms and local searching capability of ant colony algorithm. The proposed IOA incorporates ant colony algorithm for exploration and evolutionary algorithm for exploitation, and uses real-time information during the optimization process. In order to discuss the performance of the proposed algorithm, a mixed integral programming model for DVRP is formulated, and benchmark functions are constructed. Detailed simulation results and comparisons with the existed work show that the proposed IOA algorithm can achieve a higher performance gain, and is well suited to problems containing dynamic network environment and real-time customer requests.