Demand coverage diversity based ant colony optimization for dynamic vehicle routing problems

Dynamic vehicle routing problem (DVRP) has attracted increasing attention due to its wide applications in logistics. Compared with the static vehicle routing problem, DVRP is characterized by the prior unknown customer requests dynamically appearing in route execution. Nevertheless, the newly appeared customers pose a great challenge to route optimizer, since the optimized route may be contrarily of bad quality when including the new customers that are far from planned routes in route planning. To address this issue, in this paper we propose a demand coverage diversity based metaheuristic, termed ACO-CD, in the framework of ant colony algorithm. In ACO-CD, a demand coverage diversity adaptation method is suggested to maintain the diversity of covered customers in routes so that the optimizer can effectively response to the newly appeared customer requests. Experimental results on 27 DVRP test instances demonstrate the effectiveness of the proposed demand coverage diversity adaptation method and the superiority of the proposed ACO-CD over four state-of-the-art DVRP algorithms in terms of solution quality.