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

Glider Routing and Trajectory Optimisation in disaster assessment

In this paper, we introduce the Glider Routing and Trajectory Optimisation Problem (GRTOP), the problem of simultaneously finding optimal routes and trajectories for a fleet of gliders with the aim of surveying a set of locations. We propose a novel Mixed-Integer Nonlinear Programming (MINLP) formulation for the GRTOP, which optimises the routes as well as the trajectories along these routes, while flight dynamics is modelled as constraints. We avoid solving a non-convex problem by linearising the gliders’ flight dynamics, converting the proposed MINLP into a Mixed-Integer Second-order Cone Programming (MISOCP) problem. To allow for a more tractable formulation, the dynamical constraints are relaxed and a penalisation is added to the objective function. Several different discretisation techniques are compared. The formulation is tested on instances inspired by risk maps of flooding-prone cities across the UK and on 180 randomly generated instances.</p

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

Instances for the GRTOP

This dataset corresponds to the Glider Routing and Trajectory Optimisation Problem (GRTOP), the problem of simultaneously finding optimal routes and trajectories for a fleet of gliders with the aim of surveying a set of locations. It can be used for testing mathematical formulations and algorithms for solving the GRTOP. This dataset has been artificially generated based on risk maps of flooding-prone cities across the UK. This supports the paper: Pereira Coutinho, Walton, Fliege, Jörg and Battarra, Maria (2017) Glider Routing and Trajectory Optimisation in Disaster Assessment

Instances for the GRTOP

This dataset corresponds to the Glider Routing and Trajectory Optimisation Problem (GRTOP), the problem of simultaneously finding optimal routes and trajectories for a fleet of gliders with the aim of surveying a set of locations. It can be used for testing mathematical formulations and algorithms for solving the GRTOP. This dataset has been artificially generated based on risk maps of flooding-prone cities across the UK. This supports the paper: Pereira Coutinho, Walton, Fliege, J&ouml;rg and Battarra, Maria (2017) Glider Routing and Trajectory Optimisation in Disaster Assessment.</span