Knowledge-driven ant colony optimization algorithm for vehicle routing problem in instant delivery peak period

Instant delivery is an important part of urban logistics distribution, which realizes point-to-point distribution between merchants and customers. During the peak period of orders, instant delivery is a large-scale variable NP-hard combinatorial optimization problem, which increases the difficulty and complexity of scheduling greatly. To solve the large-scale vehicle routing problem of instant delivery in peak periods, a knowledge-driven ant colony optimization (KDACO) algorithm is proposed in this paper. First, the knowledge base is established to guide evolutionary search, including the knowledge of order priority and the feature knowledge of feasible schemes. Second, the pheromone supplementation strategy is designed based on the knowledge of order priority, enhancing the guiding ability of the pheromone table. Third, the adaptive evolutionary operator is designed based on the feature knowledge of feasible schemes, improving the optimization efficiency of the algorithm. Finally, numerical experiments on extensive classical datasets show that the proposed KDACO can obtain superior performance to other state-of-the-art algorithms in the instant delivery peak period.