Glider routing and trajectory optimisation in disaster assessment
Glider routing and trajectory optimisation in disaster assessment
In this paper, we introduce the Glider Routing and Trajectory Optimisation Problem (GRTOP), the problem of finding optimal routes and trajectories for a fleet of gliders with the mission of surveying a set of locations. We propose a novel MINLP formulation for the GRTOP. In our approach, we consider the gliders' flight dynamics during the definition of the routes. In order to achieve better convergence, we linearise the gliders' dynamics and relax the dynamic constraints of our model, converting the proposed MINLP into a MISOCP. Several different discretisation techniques and solvers are compared. The formulation is tested on 180 randomly generated instances. In addition, we solve instances inspired by risk maps of flooding-prone cities across the UK.
OR in disaster relief, unmanned gliders, routing, trajectory optimisation
Pereira Coutinho, Walton
844cd0ea-6cef-45fd-98f6-906493797077
Fliege, Jörg
54978787-a271-4f70-8494-3c701c893d98
Battarra, Maria
0498dc58-e9d5-4ad2-a141-040f7bcebbc2
Pereira Coutinho, Walton
844cd0ea-6cef-45fd-98f6-906493797077
Fliege, Jörg
54978787-a271-4f70-8494-3c701c893d98
Battarra, Maria
0498dc58-e9d5-4ad2-a141-040f7bcebbc2
Pereira Coutinho, Walton, Fliege, Jörg and Battarra, Maria
(2018)
Glider routing and trajectory optimisation in disaster assessment.
European Journal of Operational Research.
(doi:10.1016/j.ejor.2018.10.057).
Abstract
In this paper, we introduce the Glider Routing and Trajectory Optimisation Problem (GRTOP), the problem of finding optimal routes and trajectories for a fleet of gliders with the mission of surveying a set of locations. We propose a novel MINLP formulation for the GRTOP. In our approach, we consider the gliders' flight dynamics during the definition of the routes. In order to achieve better convergence, we linearise the gliders' dynamics and relax the dynamic constraints of our model, converting the proposed MINLP into a MISOCP. Several different discretisation techniques and solvers are compared. The formulation is tested on 180 randomly generated instances. In addition, we solve instances inspired by risk maps of flooding-prone cities across the UK.
Text
GRTOP
- Accepted Manuscript
More information
Submitted date: 5 August 2017
Accepted/In Press date: 29 October 2018
e-pub ahead of print date: 8 November 2018
Keywords:
OR in disaster relief, unmanned gliders, routing, trajectory optimisation
Identifiers
Local EPrints ID: 426092
URI: http://eprints.soton.ac.uk/id/eprint/426092
ISSN: 0377-2217
PURE UUID: 93377635-f3df-44b1-8185-a8cc043adeb0
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Date deposited: 14 Nov 2018 17:30
Last modified: 16 Mar 2024 07:17
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Contributors
Author:
Walton Pereira Coutinho
Author:
Maria Battarra
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