A multi-objective green UAV routing problem

• Published in: Computers & operations research Vol. 88; pp. 306 - 315
• Main Authors: Coelho, Bruno N., Coelho, Vitor N., Coelho, Igor M., Ochi, Luiz S., Haghnazar K., Roozbeh, Zuidema, Demetrius, Lima, Milton S.F., da Costa, Adilson R.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.12.2017; Pergamon Press Inc
• Subjects: Autonomy; Decision making; Drone aircraft; Drone vehicles; Integer programming; Linear programming; Matheuristic; Microgrids; Multi-objective optimization; Multiple objective analysis; Operations research; Route optimization; Route planning; Search algorithms; Studies; Unmanned aerial vehicle; Unmanned aerial vehicles; Vehicle routing; Vehicles; Multi-objective optimization; Matheuristic; Unmanned aerial vehicle; Vehicle routing; Microgrids
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2017.04.011

•Introduce a new time-dependent UAV heterogeneous fleet routing problem.•Consider several objective functions and respect drones operational requirements.•Design a MILP model in order to find sets of non-dominated solutions.•Consider a model able to tackle multi-layer scenarios with package exchanging points.•Integrate UAV into the new concepts of mini/microgrid systems inside smart cities. This paper introduces an Unmanned Aerial Vehicle (UAV) heterogeneous fleet routing problem, dealing with vehicles limited autonomy by considering multiple charging stations and respecting operational requirements. A green routing problem is designed for overcoming difficulties that exist as a result of limited vehicle driving range. Due to the large amount of drones emerging in the society, UAVs use and efficiency should be optimized. In particular, these kinds of vehicles have been recently used for delivering and collecting products. Here, we design a new real-time routing problem, in which different types of drones can collect and deliver packages. These aerial vehicles are able to collect more than one deliverable at the same time if it fits their maximum capacity. Inspired by a multi-criteria view of real systems, seven different objective functions are considered and sought to be minimized using a Mixed-Integer Linear Programming (MILP) model solved by a matheuristic algorithm. The latter filters the non-dominated solutions from the pool of solutions found in the branch-and-bound optimization tree, using a black-box dynamic search algorithm. A case of study, considering a bi-layer scenario, is presented in order to validate the proposal, which showed to be able to provide good quality solutions for supporting decision making.