Drone-assisted collaborative routing for last-mile delivery (LMD) has been the subject of a lot of recent research. This article introduces a brand-new LMD optimization problem for cooperative routing involving a truck and a fleet of drones. A new method for coordinating a vehicle and a fleet of drones to serve a certain group of consumers is suggested by the challenge. The strategy is focused on figuring out where to park the truck and where to send the drones to service the consumers. The best way to find these parking lots is provided. A mixed linear integer programming formulation is offered for the transportation network, which comprises these points, with the goal of reducing the time it takes to serve every client. To examine the qualities of the model solutions and establish their computational bounds, computational experiments were performed on a collection of issue instances. Experiments revealed that the suggested methodology might greatly enhance outcomes over using just a vehicle for logistics. We also suggest a Greedy Randomized Adaptive Search Procedure (GRASP) met heuristic to solve larger-scale situations. Instances with various features were used to study how well it performed computationally. The study proposes future research ideas and analyses some findings from computational experimentation

drones routing, collaborative, truck-drone, and parking lots.