A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement

Large-scale dynamic vehicle routing problem (LSDVRP) is exhibiting extensive application prospect with the rapid growth of online logistics, whereas a few approaches have been developed to address LSDVRPs. The difficulty in solving LSDVRPs lies in that it requires quick response and high adaptability to numerous newly appeared customers in LSDVRPs. To overcome this difficulty, in this paper, we propose a responsive ant colony optimization algorithm, termed as RACO, for efficiently addressing LSDVRPs. In the proposed RACO, a pheromone diversity enhancing method is suggested to generate diverse pheromone matrices for quickly responding to newly appeared customer requests in solving LSDVRPs. A pheromone ensemble technique is further designed to produce a high-quality initial population that well adapts to the new customer requests by making use of diverse pheromone matrices. Empirical results on a set of 12 LSDVRP test instances demonstrate the effectiveness of the suggested pheromone diversity enhancing method in quickly responding to newly appeared customer requests for solving LSDVRPs. Moreover, we investigate the computational cost and the traveling cost obtained by the proposed RACO to evaluate responsiveness and adaptability of the proposed RACO, respectively. Comparison with four state-of-the-art approaches to DVRPs validates the superiority of the proposed RACO in addressing LSDVRPs in terms of responsiveness and adaptability.