A Simulation Framework for Evaluating Airline Temporary Schedule Adjustments Following Incidents

Yan, Shangyao; Tang, Chin Hui; Shieh, Chong Lan

doi:10.1080/03081060500120324

Cited by 14 publications

(3 citation statements)

References 9 publications

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“…On the other hand, there were several simulation studies analyzing the problems related specifically to air transportation infrastructure, operations and airports. Some topics that were evaluated in these studies were the improvement of airline schedules (Yan et al , 2005), conflicting ground movements (Pitfield et al , 1998), airport capacity estimation (Pitfield and Jerrard, 1999), management of the runway and taxiways (Ottman et al , 1999), construction of a new terminal building (Kiran et al , 2000), air traffic control (Callantine et al , 2001), airport gate planning (Hamzawi, 1986; Yan et al , 2002), airport road network, transportation modes to airports and parking (Psaraki and Abacoumkin, 2002), modeling the flow of passengers in airport terminals (Gatersleben and Van der Weij, 1999; Takakuwa and Oyama, 2003), air cargo operations (Nsakanda et al , 2004), contingency planning for aircraft turnaround operations (Adeleye and Chung, 2006), airport luggage retrieval (Rijsenbrij and Ottjes, 2007), aircraft operations (Mott et al , 2016) and airline operations control (Bouarfa et al , 2016). Zhang et al (2016) studied topological properties of the Chinese airline network and air route connections influence on it.…”

Section: Literature Surveymentioning

confidence: 99%

Simulating air transportation networks under capacity constraints

Karaman

2018

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Purpose Congestion as a consequence of the rapidly growing air traffic is one of the outstanding problems of the air transportation industry. Traffic impediment rates, having an increasing acceleration while the airport capacities have been kept constant due to several reasons, cause problems such as arrival/departure delays, schedule interruptions, cancellations and customer dissatisfaction. In this paper, the author aims to study transitioning from a single-hub air transportation system to a multi-hub infrastructure via Monte Carlo simulation. Design/methodology/approach The current hub has reached its capacity limits for long so that the growth potential of the air transportation has been affected adversely. One of the possible remedies suggested by authorities, professionals and academics was to transform the air transportation infrastructure into a multi-hub setting. Current air traffic of the country was modeled by means of simulation. Airport capacities and performances are simulated and analyzed under different scenarios considering a potential alternative hub along with the central one. Possible delays in both hubs are studied in case of moderately increasing traffic congestion. Findings As a result, decreased delay levels in the central hub are observed, whereas no delays are experienced in the potential one in all the scenarios. Originality/value This study, proposing to organize the national and international air traffic of the country while harmonizing the delay rates and increasing the passenger satisfaction, is to contribute significantly to the aviation sector companies, airliners and airport operators by shedding light on the imminent capacity issues air transportation industry is going to face.

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Section: Literature Surveymentioning

confidence: 99%

Simulating air transportation networks under capacity constraints

Karaman

2018

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“…To produce the recovery plan and solve the disruption problem, a connection network [3]- [7], a time-line network [8]- [11], a time-band network [12]- [14] and an inequality-based multi-objective genetic algorithm [2] are proposed respectively in literature.…”

Section: Introductionmentioning

confidence: 99%

Threat methodology for securing scalable video in the Cloud

Dang

Zheng

2013

2013 IEEE Third International Conference on Information Science and Technology (ICIST)

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In this paper, a distributed implementation of Dang's iterative method [1] is proposed to solve the airline disruption problems caused by the airport closures. The problem consists of two subproblems, and the solution domain of the first subproblem is divided into several segments by the initial seeds cluster division method. Dang's method [1] is applied to solve the first subproblem in each segment simultaneously in a distributed computation network, and the obtained feasible flight routes are used to solve the second subproblem. Numerical results show that the proposed method is better than CPLEX and Liu et al. [2].

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“…Most research on recovery scheduling has focused on enhancing static recovery scheduling models, while none has analyzed these factors from a systems perspective. Yan et al (2005) proposed a framework embodying a simulation process. Their framework not only analyzes the effect of stochastic flight delays on static recovery scheduling, but also designs more effective flexible buffer times and real-time schedule recovery rules.…”

Section: Airline Schedule Disruption Problemmentioning

confidence: 99%

Disruption Management of an Inequality-Based Multi-Fleet Airline Schedule by a Multi-Objective Genetic Algorithm

Liu

Jeng

Chang

2008

Transportation Planning and Technology

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This paper presents a novel application of a Method of Inequalitybased Multi-objective Genetic Algorithm (MMGA) to generate an efficient timeeffective multi-fleet aircraft routing algorithm in response to the schedule disruption of short-haul flights. It attempts to optimize objective functions involving ground turn-around times, flight connections, flight swaps, total flight delay time and a 30-minute maximum delay time of original schedules. The MMGA approach, which combines a traditional Genetic Algorithm (GA) with a multi-objective optimization method, can address multiple objectives at the same time, then explore the optimal solution. The airline schedule disruption management problem is traditionally solved by Operations Research (OR) techniques that always require a precise mathematical model. However, airline operations involve too many factors that must be considered dynamically, making a precise mathematical model difficult to define. Experimental results based on a real airline flight schedule demonstrate that the proposed method, Transportation Planning and Technology, December 2008 Vol. 31, No. 6, pp. 613Á639 Multi-objective Optimization Airline Disruption Management by GA, can recover the perturbation efficiently within a very short time. Our results further demonstrate that the application can yield high quality solutions quickly and, consequently, has potential to be employed as a real-time decision support tool for practical complex airline operations.

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A Simulation Framework for Evaluating Airline Temporary Schedule Adjustments Following Incidents

Cited by 14 publications

References 9 publications

Simulating air transportation networks under capacity constraints

Simulating air transportation networks under capacity constraints

Threat methodology for securing scalable video in the Cloud

Disruption Management of an Inequality-Based Multi-Fleet Airline Schedule by a Multi-Objective Genetic Algorithm

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