GATES: an airline gate assignment and tracking expert system

Brazile, Robert; Swigger, Kathleen

doi:10.1109/64.2103

Cited by 47 publications

(10 citation statements)

References 2 publications

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“…To that purpose the use of metadata and expert systems [20][21][22] have been proposed. The former is used for model annotation, which is then used for storage, search, and retrieval of the models from a library (database).…”

Section: A Intelligent Model Selection In the Assembly Of Workflowsmentioning

confidence: 99%

Composition, Management, and Exploration of Computational Studies at Early Design Stage

Guenov¹,

Nunez²,

Molina-Cristóbal³

et al. 2014

Computational Intelligence in Aerospace Sciences

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I. INTRODUCTIONExploring the design space with the aim of finding feasible, let alone optimal solutions is not trivial. Essentially, this involves finding a solution to an inverse problem. That is, a relatively small number of (presumably) known requirements and performance characteristics need to be mapped onto a much larger number (space) of unknown design parameters, subject to constraints. The inverse problem, of course, is generic and forms parts of the study of complex systems, from ecology [1,2] to engineering design [3], where the integration of an expanding set of (validated) numerical methods and models is used to investigate scenarios and to predict outcomes.The work presented here lies within this context, with a particular emphasis on computational intelligence methods and tools for interactive design space exploration. The scope is restricted (but not limited) to conceptual computational design where a complex product, for example aircraft, ship, and so on, is described by a large number of computational models related to geometry parameterization, performance, cost, and so forth. It is assumed that the computational models are black-boxes (e.g., compiled code) which contain low-fidelity code (e.g., parametric/empirical equations) and/or surrogate models. This assumption reflects the realities of the commercial world in which the content of a model is usually a closely guarded intellectual property.There are a number of challenges associated with such complex and relatively little studied computational systems:

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Section: A Intelligent Model Selection In the Assembly Of Workflowsmentioning

confidence: 99%

Composition, Management, and Exploration of Computational Studies at Early Design Stage

Guenov¹,

Nunez²,

Molina-Cristóbal³

et al. 2014

Computational Intelligence in Aerospace Sciences

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show abstract

“…GATES has been designed for a PC environment. Although the expert system is operational and well received, future expansions aim to a better graphic interphase and to incorporate features enabling learning from experience [10].…”

Section: Survey Of Engineering Based Expert Systemsmentioning

confidence: 99%

Knowledge-based (expert) systems in engineering applications: A survey

Valavanis

Kokkinaki

Tzafestas

1994

J Intell Robot Syst

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“…The problem of airport gate scheduling has been addressed in several knowledge-based systems, such as those presented in [6], [7], [8], and [9]. All of these projects use domain knowledge and heuristics captured from human experts (and in some cases configurable by the user) to generate an optimal scheduling of airport gates.…”

Section: Discussion and Related Workmentioning

confidence: 99%

A coevolutionary method for automating airport gate scheduling

Garza

Licastro

Proceedings of the Fourth Mexican International Conference on Computer Science, 2003. ENC 2003.

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The problem of assigning gates to aircraft that are due to arrive at an airport is one that involves a dynamic task environment. Airport gates can only be assigned if they are currently available, but deciding which gate to assign to which flight also involves satisfying multiple additional constraints. Once a solution has been found, new incoming flights will have approached the airspace of the airport in question, and these will require arrival gates to be assigned to them, so the entire process must be repeated. These observations have led us to propose a coevolutionary model for automating the airport gate scheduling problem.We represent the genotypes of two species. One species corresponds to the current problem to be addressed (a list of departing and arriving flights at a given time-step) and the other species corresponds to the solutions being proposed for that problem (a list of possible gate assignments for the arriving flights). An evolutionary algorithm which operates on a population of solution genotypes is used to solve each instance of the airport gate scheduling problem. A coevolutionary algorithm in which the two species influence each other, which incorporates the previously-mentioned evolutionary algorithm once at each time-step, models the fact that multiple instances of the problem occur over time as an airport operates.

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GATES: an airline gate assignment and tracking expert system

Cited by 47 publications

References 2 publications

Composition, Management, and Exploration of Computational Studies at Early Design Stage

Composition, Management, and Exploration of Computational Studies at Early Design Stage

Knowledge-based (expert) systems in engineering applications: A survey

A coevolutionary method for automating airport gate scheduling

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