A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

Abstract

With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself.We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-firstserved ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches.

Application of Improved Q-Learning Algorithm in Dynamic Path Planning for Aircraft at Airports

“…C Evertse [4] used mixed-integer linear programming (MILP) to plan taxiing routes for all aircraft on the airport surface, aiming to minimize the taxiing time of aircraft on the ground. Brownlee et al [5] combined genetic algorithms with the rolling window approach to solving the allocation of taxiing routes for aircraft at the airport. In 2022, Deng W et al [6] proposed a multistrategy particle swarm and ant colony optimization algorithm, MPSACO, to solve the airport runway planning problem and to avoid conflicts between taxiways and the propagation of conflicts.…”

Conflict-free routing of multi-stop warehouse trucks

“…The second group of approaches (e.g. [3,4,16,17,19]) tend to use a variation of shortest path algorithms such as Dijkstra or A * , with an addition of a time element to only construct collision-avoiding routes. The closest to the proposed approach is Vivaldini et al [19], in that it also includes a toplevel heuristic to allocate jobs to trucks.…”

“…This can be formulated as a search over permutations, the well-known Travelling Salesman Problem, an instance of which exists for each collection trip. Furthermore, the order in which routes are allocated to vehicles has been shown to make a small, but nevertheless notable, difference to total transit times for QPPTW [4,17]. Simply, because the routes are reserved via the time-windows, it is possible for an earlier truck to be routed in such a way that it prevents several later trucks being routed optimally.…”

“…Earlier work has proposed conflict-free routing approaches based on variations of the well-known Dijkstra's [3,17] and A* [4,16,19] shortest path algorithms. The focus in this article is on the conflict-free routing algorithm QPPTW [17], a variant of Dijkstra's shortest path algorithm that accounts for the times of previous vehicle movements.…”