Supporting data for "Truck-and-Drone System for Multi-type Rescue Services after Disasters"

This study is about a truck-and-drone system for delivery and surveillance tasks in disaster responses. We focused on optimizing routes of trucks and drones to minimize the total priority cost of serving tasks, which is defined as the sum of the priority coefficients multiplied by the task service start time. This study investigates multiple problems, including a deterministic problem, a robust problem with travel time uncertainties, and a dynamic routing problem with demand uncertainties.

This dataset includes data for the thesis titled "Truck-and-Drone System for Multi-type Rescue Services after Disasters". We have included the numerical results in the dataset, including our algorithm compared with Gurobi, benefits of flexible truck-drone collaborations, benefits of robust routing, and performance of dynamic routing methods. In addition, the Python code for processing the data is also included in the dataset. Code is with necessary comment for understanding.