Aircraft Sequencing Problems via a Rolling Horizon Algorithm

Furini, Fabio; Persiani, Carlo Alfredo; Toth, Paolo

doi:10.1007/978-3-642-32147-4_25

Cited by 21 publications

(14 citation statements)

References 7 publications

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“…The total runtime to solve all problem instances to optimality was 64 milliseconds, or on average 5.3ms per problem instance. This equates to a speedup by a factor of 37170 relative to the runtimes reported in Table 4 Optimal results for the benchmark instances introduced by Furini et al (2012).…”

Section: Comparison With Previous Approachesmentioning

confidence: 99%

Pruning Rules for Optimal Runway Sequencing

Maere¹,

Atkin²,

Burke³

2018

Transportation Science

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This paper investigates runway sequencing for real world scenarios at one of the world's busiest airports, London Heathrow. Several pruning principles are introduced that enable significant reductions of the problem's average complexity, without compromising the optimality of the resulting sequences, nor compromising the modelling of important real world constraints and objectives. The pruning principles are generic and can be applied in a variety of heuristic, meta-heuristic or exact algorithms. They could also be applied to different runway configurations, as well as to different variants of the machine scheduling problem with sequence dependent setup times, the generic variant of the runway sequencing problem in this paper. They have been integrated into a dynamic program for runway sequencing, which has been shown to be able to generate optimal sequences for large scale problems at an extremely low computational cost, whilst considering complex non-linear and non-convex objective functions that offer significant flexibility to model real world preferences and real world constraints. The results shown here counter the proliferation of papers that claim that runway sequencing problems are too complex to solve exactly and therefore attempt to solve them heuristically.

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Section: Comparison With Previous Approachesmentioning

confidence: 99%

Pruning Rules for Optimal Runway Sequencing

Maere¹,

Atkin²,

Burke³

2018

Transportation Science

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“…However, in order to meet computing time limits for instances of practical interest, big-M MIP approaches typically need to resort to some sort of heuristic decomposition of the problem (for instance, rolling horizon, see e.g. Furini et al (2012) and Samà et al (2013)), and the overall algorithm boils down to a heuristic method.…”

Section: Management (Dman) Arrival Management (Aman) and Surface Manmentioning

confidence: 99%

“…However, in many practical contexts, larger sizes can still provide satisfactory solutions, allowing for drastic reductions in computing times. For example, in Furini et al (2012) the step size is 60 seconds for all instances. In Heidt et al (2013), in the same instance the authors resort to two distinct step sizes, namely 5 seconds for (the first) 10 airplanes and 75 seconds for the remaining ones.…”

Section: Management (Dman) Arrival Management (Aman) and Surface Manmentioning

confidence: 99%

Time-Indexed Formulations for the Runway Scheduling Problem

Avella

Boccia

Mannino

et al. 2017

Transportation Science

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The problem of sequencing and scheduling airplanes landing and taking off on a runway is a major challenge for air traffic management. This difficult real-time task is still carried out by human controllers, with little help from automatic tools. Several methods have been proposed in the literature, including Mixed Integer Programming (MIP) based approaches. However, in a recent survey (Bennell et al. (2011)) MIP is claimed to be unattractive for real-time applications, since computation times are likely to grow too large. In this paper we reverse this claim, by developing a MIP approach able to solve to optimality real-life instances from congested airports in the stringent times allowed by the application. In order to achieve this it was mandatory to identify new classes of strong valid inequalities, along with developing effective fixing and lifting procedures.

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“…Gavranis and Kozanidis [3] designed the flight assignment algorithm with flight delay. Furini et al [4] optimized the flight sequencing problem using a rolling horizon algorithm. In [5], a rolling horizon algorithm is proposed for the aircraft landing sequence problem.…”

Section: Introductionmentioning

confidence: 99%

Flights Assignment Model of Multiple Airports Based on Game Theory and CDM Mechanism

Liu

2020

Mathematical Problems in Engineering

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To minimize delay cost of flights in multiple airports, this paper studied flights assignment problem under delay conditions. By considering the delay cost, airport capacity, and the slot exchange between airlines, this paper proposed a novel assignment model based on game theory and CDM mechanism. An improved ant colony algorithm was proposed to solve the flight assignment problem. The case studies showed that the proposed model can optimize the minimum delay cost between airlines under multiairport capacity constraints. The performance of proposed method was better than that of traditional non-slot-exchange method.

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Aircraft Sequencing Problems via a Rolling Horizon Algorithm

Cited by 21 publications

References 7 publications

Pruning Rules for Optimal Runway Sequencing

Pruning Rules for Optimal Runway Sequencing

Time-Indexed Formulations for the Runway Scheduling Problem

Flights Assignment Model of Multiple Airports Based on Game Theory and CDM Mechanism

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