Collaborative Truck-Drone Routing Optimization Using Quantum-Inspired Genetic Algorithms - Yetis, Hasan; Karakose, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2021)
Today, e-commerce has become more common than ever. The time for customers to receive the product they ordered is directly dependent on the speed of the shipment companies. In addition to operating and operator costs, traditional deliveries also increase delivery times. That's why the distribution with truck-drone cooperation method emerged. In this study, it is aimed to determine the less costly route for parcel delivery with the cooperation of truck-drone. Quantuminspired genetic algorithms are used to optimize the route. The proposed method is tested with truck-drone simulation developed with Python programming language. Simulation results are obtained and compared exclusively for truck-based distribution and distribution with truck-drone collaboration. A cost calculation is made by taking into account factors such as the total distance traveled, the waiting time of the customers, and the working time of the staff. The simulation results show that despite the increasing total traveling distance in the truck-drone collaboration, cost savings are achieved. As a result of the tests carried out, it is seen that 4-8% cost savings are achieved from 3 different scenarios created for 100 customers. © 2021 IEEE.
25th International Conference on Information Technology, IT 2021 -- 16 February 2021 through 20 February 2021 -- Zabljak -- 168281
Keywords: Computer simulation languages; Computer software; Drones; Routing algorithms; Sales; Trucks; Cooperation method; Cost calculation; Operator costs; Parcel delivery; Python programming language; Quantum inspired genetic algorithm; Routing optimization; Total distances; Genetic algorithms