A multi-objective humanitarian pickup and delivery vehicle routing problem with drones

This paper applies the truck and drone cooperative delivery model to humanitarian logistics and proposes a multi-objective humanitarian pickup and delivery vehicle routing problem with drones (m-HPDVRPD) which contains two subproblems: cooperative routing subproblem and relief supplies allocation subproblem. The m-HPDVRPD is formulated as a multi-objective mixed integer linear programming (MILP) model with two objectives, which simultaneously minimizes the maximum cooperative routing time and maximizes the minimum fulfillment rate of demand nodes. We develop a hybrid multi-objective evolutionary algorithm with specialized local search operators (HMOEAS) and a hybrid multi-objective ant colony algorithm (HACO) for the problem. A set of numerical experiments are performed to compare the performance of the two algorithms and their three variants. And the experimental results prove that HMOEAS is more effective than other methods. Taking the Corona Virus Disease 2019 (COVID-19) epidemic in Wuhan as a case, we compare the truck-drone cooperative delivery model with the truck-only delivery and the drone-only delivery models, and find that our model has advantages in the delivery efficiency of anti-epidemic materials. Moreover, based on the real-world case, a sensitivity analysis is conducted to investigate the impact of different drone parameters on the efficiency of truck-drone cooperative delivery.