Breakout local search for the multi-objective gate allocation problem

This work was partially supported by the research project EP/H004424/2

A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process

A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process

A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem

Employee scheduling problems are of critical importance to large businesses. These problems are hard to solve due to large numbers of conflicting constraints. While many approaches address a subset of these constraints, there is no single approach for simultaneously addressing all of them. We hybridise 'Evolutionary Ruin & Stochastic Recreate' and 'Variable Neighbourhood Search' metaheuristics to solve a real world instance of the employee scheduling problem to near optimality. We compare this with Simulated Annealing, exploring the algorithm configuration space using the irace software package to ensure fair comparison. The hybrid algorithm generates schedules that reduce unmet demand by over 28% compared to the baseline. All data used, where possible, is either directly from the real world engineer scheduling operation of around 25,000 employees , or synthesised from a related distribution where data is unavailable

Efficient Inter-Team Task Allocation in RoboCup Rescue

The coordination of cooperative agents involved in rescue missions is an important open research problem. We consider the RoboCup Rescue Simulation (RCS) challenge, where teams of agents perform urban rescue operations. Previous approaches typically cast such problem as separate single-team allocation problems. However, different teams have complementary capabilities, and therefore some kind of inter-team coordination is desirable for high-quality solutions. Our contribution considers inter-team coordination using Max-Sum. We present a methodology that allows teams in RCS to efficiently assess joint allocations. Furthermore, we show how to reduce the algorithm's computational complexity from exponential to polynomial time by using Tractable High Order Potentials. To the best of our knowledge this is the first time where it has been shown that MS can be run in polynomial time in the RCS challenge without relaxing the problem. Experiments with fire brigades and police agents show that teams employing inter-team coordination are significantly more effective than uncoordinated teams. Moreover, the evaluation shows that our BMS and THOPs method achieves up to 2.5 times better results than other state-of-the-art methods. Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems.Work funded by projects DAMAS (TIN2013-45732-C4-4-P), COR (TIN2012-38876-C02-01), the Generalitat of Catalunya grant 2009-SGR-1434, and the Ministry of Economy and Competitivity grant BES-2010-030466.Peer reviewe

Methods And Sources For Underpinning Airport Ground Movement Decision Support Systems

The airport Ground Movement problem is concerned with the allo- cation of routes to aircraft for their travel along taxiways between the runway and the stands. It is important to find high quality solutions to this problem because it has a strong influence on the capacity of an airport and upon the environmental impact. The problem is particularly challenging. It has multiple objectives (such as minimising taxi time and fuel consumption). It also has considerable uncertainty, which arises from the complex operations of an airport. It is an active and topical area of research. A barrier to scientific research in this area is the lack of publicly available realistic data and benchmark problems. The reason for this is often concerned with commercial sensitivities. We have worked with airports and service providers to address this issue, by exploring several sources of freely-available data and developing algorithms for cleaning and processing the data into a more suitable form. The result is a system to generate datasets that are realistic, and that facilitate research with the potential to improve on real-world problems, without the confidentiality and commercial licensing issues usually associated with real airport data. Case studies with several international airports demonstrate the usefulness of the datasets. The algorithms have been implemented within three tools and made freely-available for researchers. A benchmark Ground Movement problem has also been made available, with results for an existing Ground Movement algorithm. It is intended that these contributions will underpin the advance of research in this difficult application area

A repülőtéri állóhelyek kiosztásának optimalizálása többcélú lineáris programozással = Optimizing Airport Stand Allocation Using Multi Objective Linear Programming

A repülőtéri állóhelyek kiosztása többcélú optimalizálási folyamat, amely hatással van az üzemeltetés hatékonyságára, és gyors beavatkozást igényel változó körülmények esetén (pl. késések kezelése kedvezőtlen időjárási körülmények esetén), különösen a forgalmas repülőtereken. Cikkünkben modellezzük és optimalizáljuk a repülőtéri állóhelyek kiosztását, ami a cikk tudományos értéke. Bemutatjuk a repülőtéri állóhelyek kiosztásának folyamatát és problémáját, különös tekintettel a kiosztást befolyásoló tényezőkre. Kidolgozzuk az állóhelyek kiosztásának lineáris programozási modelljét, meghatározzuk a korlátokat és a célfüggvényeket, figyelembe véve többek között az állóhely használati költségeket és az utasok gyaloglási idejét. Egy fiktív repülőtéren modelleztük a különböző állóhely-típusokat, és optimalizáltuk az állóhelyek kiosztását. A többcélú optimalizálás célfüggvényinek súlyát változtatva négy esetet vizsgáltunk. A súlyozás eredményeként az állóhelyek elosztása többféleképpen is optimalizálhatóvá vált. Airport stand allocation is a multi-purpose optimization process that has an impact on operational efficiency and requires rapid intervention in case of changing conditions (e.g., managing delays in case of severe weather conditions), especially at busy airports. In our article, we model and optimize airport stand allocation, which is the scientific value. We present the process and the problem of airport stand allocation, with a special focus on factors influencing the allocation. We develop a linear programming model of stand allocation, define constraints and objective functions considering parking (stand usage) cost and passenger walking time among others. A fictitious airport was modelled, and the stand allocation was optimized. Four weighting cases were examined. As a result of weighting, the allocation of stands can be optimized in several ways

A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing

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

A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing

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-first-served ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches