An Adaptive Memetic Algorithm for a Cost-Optimal Electric Vehicle-Drone Routing Problem

This paper considers a fleet of electric vehicles and drones that deliver goods collaboratively. To determine the optimal routes of this electric vehicle-drone routing problem, the problem is formulated as a mixed-integer linear program to minimize the total operational costs. To solve the model, we develop an adaptive memetic algorithm that employs a multi-operator concept with a Q-learning-based selection mechanism and a set of local search operators for exploring the complex search space of the problem. Using extensive numerical experiments, we prove the effectiveness of our proposal and reveal some interesting managerial insights.