Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/9118
Title: A multi-objective green UAV routing problem.
Authors: Coelho, Bruno Nazário
Coelho, Vitor Nazário
Coelho, Igor Machado
Ochi, Luiz Satoru
Koochaksaraei, Roozbeh Haghnazar
Zuidema, Demetrius
Lima, Milton Sérgio Fernandes de
Costa, Adilson Rodrigues da
Keywords: Unmanned aerial vehicle
Vehicle routing
Microgrids
Multi-objective optimization
Issue Date: 2017
Citation: COELHO, B. N. et al. A multi-objective green UAV routing problem. Computers & Operations Research, v. 01, p. 306–315, 2017. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0305054817301028>. Acesso em: 29 set. 2017.
Abstract: 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 re- quirements. A green routing problem is designed for overcoming difficulties that exist as a result of lim- ited 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 deliver- ing 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 deliver- able 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 Lin- ear 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 val- idate the proposal, which showed to be able to provide good quality solutions for supporting decision making.
URI: http://www.repositorio.ufop.br/handle/123456789/9118
metadata.dc.identifier.uri2: http://www.sciencedirect.com/science/article/pii/S0305054817301028
ISSN:  03050548
Appears in Collections:DEMET - Artigos publicados em periódicos

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