A novel multi-objective optimization model for vehicle routing problem with drone delivery and dynamic flight endurance

In these instances, the number of customers is set to 40, 80, 120, and 160 respectively. All customers and depot are located in a square area. The vertical distance from the edge of the area to the center of the area is set to 20 km, 30 km, and 40 km, which represent the three different sizes of square areas respectively. Because the maximum load capacity of drones can not satisfy the demand of all customers in the real world, the percentage of customers that can be served by drones is assumed to be 30%, 50%, and 70% respectively in these instances. Since these instances involve the different types of combinations (the number of customers, the vertical distance from the edges of square area to its center, and the percentage of customers who can be served by drones), they can be simply named intuitively. For example, the instance "40-20-30" means that there are 40 customers located in a square area, the vertical distance from the edge of the area to its center is 20 km, and only 30% of the customers can be served by the drone.

本数据集的客户数量分别设置为40、80、120及160。所有客户与配送中心（depot）均坐落于正方形区域内。该区域边缘至区域中心的垂直距离分别设为20km、30km及40km，以此对应三种不同规模的正方形作业区域。鉴于现实场景中无人机的最大负载能力无法覆盖全部客户的配送需求，本数据集分别设置可由无人机服务的客户占比为30%、50%及70%。由于本数据集的实例涵盖三类组合维度：客户数量、正方形区域边缘至区域中心的垂直距离，以及可接受无人机服务的客户占比，因此可通过直观方式对实例进行命名。例如，实例“40-20-30”表示该实例包含40个客户，其作业区域为正方形，区域边缘至中心的垂直距离为20km，且仅30%的客户可由无人机提供服务。