Scheduling aircraft landings at London Heathrow using a population heuristic

Beasley, John E.; Sonander, Julia; Havelock, P.

doi:10.1057/palgrave.jors.2601129

Cited by 128 publications

(16 citation statements)

References 13 publications

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“…"transshipment yard scheduling problem" für Umschlagbahnhöfe (etwa Bostel und Dejax, 1998;Alicke, 2002), • das sog. "aircraft landing problem" für Flughäfen (etwa Psaraftis, 1980;Ernst et al, 1999;Beasley et al, 2000Beasley et al, , 2001Beasley et al, , 2004Pinol and Beasley, 2007) und • das sog. "aircraft landing problem" für Flughäfen (etwa Psaraftis, 1980;Ernst et al, 1999;Beasley et al, 2000Beasley et al, , 2001Beasley et al, , 2004Pinol and Beasley, 2007) und • das sog.…”

Section: A Einleitungunclassified

Über die Synchronisierung von Güterströmen in der Umschlaglogistik

Boysen

2008

Z. Betriebswirtsch

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Die Synchronisierung von Warenströmen in Umschlagbahnhöfen, Cross Docks, (hub-) Flughäfen und containerhäfen, die unter dem Begriff umschlagterminals zusammengefasst werden, ist von großer praktischer Bedeutung. Auf diese Weise können kostengünstige Transportabschnitte genutzt werden, ohne den Distributionsprozess zu sehr zu verzögern. • In der wissenschaftlichen Literatur finden sich zahlreiche Optimierungsansätze zur steuerung der Warenflüsse isoliert für jede einzelne Art von umschlagsterminal. Da jedoch alle Arten von umschlagsterminals auf der gleichen logistischen grundtätigkeit beruhen, wird in diesem Aufsatz ein allgemeiner Blickwinkel angelegt. • Zunächst werden die allgemeingültigen Grundelemente von Umschlagterminals identifiziert und klassifiziert. Auf dieser Basis werden grundmodelle entwickelt, welche auf die Kernaufgabe der umschlaglogistik reduziert sind. Abschließend wird gezeigt, wie diese Grundmodelle zu lösen und anzupassen sind, um auch die Warenströme spezifischer Umschlagterminals lenken zu können.

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Section: A Einleitungunclassified

Über die Synchronisierung von Güterströmen in der Umschlaglogistik

Boysen

2008

Z. Betriebswirtsch

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“…Sabar et al [3] proposed an iterated local search algorithm, which is a single-solution-based search methodology, to minimize the total penalty of landing deviations from the target landing time. Beasley et al [4] developed a population heuristic to schedule aircraft landings at London Heathrow. In 2006, two heuristic techniques, namely, scatter search and bionomic algorithm, were presented for ALS problems [5].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Algorithm for Solving the Aircraft Landing Scheduling Problem with a Landing Sequence Division

Shi

Jiang

Liang

et al. 2019

IEICE Trans. Fundamentals

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Aircraft landing scheduling (ALS) is one of the most important challenges in air traffic management. The target of ALS is to decide a landing scheduling sequence and calculate a landing time for each aircraft in terminal areas. These landing times are within time windows, and safety separation distances between aircraft must be kept. ALS is a complex problem, especially with a large number of aircraft. In this study, we propose a novel heuristic called CGIC to solve ALS problems. The CGIC consists of four components: a chunking rule based on costs, a landing subsequence generation rule, a chunk improvement heuristic, and a connection rule. In this algorithm, we reduce the complexity of the ALS problem by breaking it down into two or more subproblems with less aircraft. First, a feasible landing sequence is generated and divided into several subsequences as chunks by a chunking rule based on aircraft cost. Second, each chunk is regenerated by a constructive heuristic, and a perturbative heuristic is applied to improve the chunks. Finally, all chunks constitute a feasible landing sequence through a connection rule, and the landing time of each aircraft is calculated on the basis of this sequence. Simulations demonstrate that (a) the chunking rule based on cost outperforms other chunking rules based on time or weight for ALS in static instances, which have a large number of aircraft; (b) the proposed CGIC can solve the ALS problem up to 500 aircraft optimally; (c) in dynamic instances, CGIC can obtain high-quality solutions, and the computation time of CGIC is low enough to enable real-time execution.

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“…So far, many studies have been conducted in order to alleviate airports from congestion and improve the capacity, some of them focused on the airspace and some other only on the airside. Concerning problems related to the airspace, we can find many works about the sequencing and merging or scheduling of arrivals in the TMA (Beasley et al, 2000;Beasley et al, 2001;Hu and Chen, 2005;Michelin et al, 2009;Balakrishnan and Chandran, 2010;Zuniga et al 2011). On the other hand we can also find studies related to ground issues such as gate assignment problem (Bolat, 2000;Dorndorf et al, 2007;Kim and Feron, 2012;Narciso and Piera, 2015), scheduling of departures (Pujet et al, 1999;Rathinam et al 2009;Sandberg et al, 2014;Simaiakis and Balakrishnan, 2015) or airport surface management (Montoya et al, 2010;.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Optimization and Simulation-Based Approach for Detecting and Resolving Conflicts at Airports

Scala

Mota

Delahaye

2018

Linköping Electronic Conference Proceedings

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In this paper is presented a methodology that uses simulation together with optimization techniques for a conflict detection and resolution at airports. This approach provides more robust solutions to operative problems, since, optimization allows to come up with optimal or suboptimal solutions, on the other hand, simulation allows to take into account other aspects as stochasticity and interactions inside the system. Both the airport airspace (terminal manoeuvring area), and airside (runway taxiways and terminals), were modelled. In this framework, different restrictions such as speed, separation minima between aircraft, and capacity of airside components were taken into account. The airspace was modeled as a network of links and nodes representing the different routes, while the airside was modeled in a low detail, where runway, taxiways and terminals were modeled as servers with a specific capacity. The objective of this work is to detect and resolve conflicts both in the airspace and in the airside and have a balanced traffic load on the ground.

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Scheduling aircraft landings at London Heathrow using a population heuristic

Cited by 128 publications

References 13 publications

Über die Synchronisierung von Güterströmen in der Umschlaglogistik

Über die Synchronisierung von Güterströmen in der Umschlaglogistik

A Heuristic Algorithm for Solving the Aircraft Landing Scheduling Problem with a Landing Sequence Division

Implementation of an Optimization and Simulation-Based Approach for Detecting and Resolving Conflicts at Airports

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