An expert system methodology for aircraft-gate assignment

Srihari, K.; Muthukrishnan, R.

doi:10.1016/0360-8352(91)90071-d

Cited by 34 publications

(16 citation statements)

References 4 publications

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“…For example, see Brazile and Swigger (1991), Srihari and Muthukrishnan (1991), Su and Srihari (1993), Gu and Chung (1999), Yan and Chang (1998), Bolat (1999Bolat ( , 2000, Yan and Huo (2001), Espinoza et al (2008), andTang (2009). However, there are differences between the gate assignment/reassignment problem and the CUCC assignment/reassignment problem, which make the models and solution algorithms developed for gate assignment/reassignment problems difficult to apply to our problem.…”

Section: Introductionmentioning

confidence: 89%

Common-use check-in counter reassignments with a variable number of service lines and variable length of time window

Yan

Tang

Chen

2014

Journal of the Chinese Institute of Engineers

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In the real world, there are a number of common factors that can cause perturbations at check-in counters, such as an airport closure, a temporary power failure, crashing of the counter computer, baggage conveyor belt malfunctions, and so on, forcing the airport authority to reassign flights to new check-in counters. In this study, we develop a model and a solution method to solve common-use check-in counter reassignment problems with a variable number of service lines and a variable length of time window. To test how well the proposed model and the solution method can be applied to the real world, we perform numerical tests based on a hypothetical incident in relation to a Taiwan airport's operations. The test results show that the model and solution would be useful for the airport authority in actual operations.

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Section: Introductionmentioning

confidence: 89%

Common-use check-in counter reassignments with a variable number of service lines and variable length of time window

Yan

Tang

Chen

2014

Journal of the Chinese Institute of Engineers

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“…The authors dealt with a multi-objective problem in which they minimize the number of flights unassigned to gates and the total walking distance. Other approaches such as expert systems [12,13] and knowledge based systems [14] were used to formulate and solve the GAP.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving airport gate assignment problem using Genetic Algorithms approach

Ghazouani

Hammami

Korbaa

2015

2015 4th International Conference on Advanced Logistics and Transport (ICALT)

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Because of the rapid growth of air traffic, optimizing airport management is becoming necessary in order to improveairport's capacity and better align its resources to the received traffic. In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers. The index of each gene represents the flight number and its value represents the gate to which the flight will be assigned. The method used to generate the initial population is based on three different heuristics and a random sorting of the gates. The selection method is the "In fitness proportionate selection" known as "roulette wheel selection". In addition to one point and two point Crossover operators, we designed a Greedy procedure Crossover (GPX) operator. The experimentation is based on the use of fictive scenarios generated in accordance with the physical characteristics of the Tunis Carthage Airport and using different flight schedules. The comparison between deterministic approach, simple heuristics and the GA has shown the efficiency of the last approach in terms of solution's quality when we aim at solving the problems of large size. Tn order to determine the best configuration of the GA, we compared the different crossover operators and we noticed that the use of GPX improves the speed of convergence of the algorithm towards better solutions.

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“…Babic et al 1984, Mangoubi and Mathaisel 1985, Vanderstraeten and Bergeron 1988, Bihr 1990, Wirasinghe and Bandara 1990, Srihari and Muthukrishnan 1991, Zhang et al 1994, Cheng 1997, Yan and Chang 1998, Haghani and Chen 1998, Bolat 1999, 2000, Yan and Huo 2001, Yan et al 2002, Ding et al 2004, 2005, Yan and Tang 2007. These models are formulated as zero-one integer (linear or quadratic) programs, mixed integer programs or network flow problems.…”

Section: Introductionmentioning

confidence: 99%

Airport gate reassignment following temporary airport closures

2009

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In this study, we develop a reassignment model for the purpose of helping airport authorities with flight-to-gate reassignments following temporary airport closures. The objective of the model is to minimise the number of gate changes subject to the related operating constraints. A preliminary evaluation of how well the model performs in practice is performed using a case study based on real operating data from the Chiang Kai-Shek International Airport in Taiwan. Several problem instances of different scales are generated to test the model's efficiency. The results show that the network model would be useful for actual operations.

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An expert system methodology for aircraft-gate assignment

Cited by 34 publications

References 4 publications

Common-use check-in counter reassignments with a variable number of service lines and variable length of time window

Common-use check-in counter reassignments with a variable number of service lines and variable length of time window

Solving airport gate assignment problem using Genetic Algorithms approach

Airport gate reassignment following temporary airport closures

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