Artificial Neural Network Modeling for Airline Disruption Management

Ogunsina, Kolawole; Okolo, Wendy

doi:10.2514/1.i011018

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Recent advancements in artificial intelligence (AI) and distributed ledger technology (DLT) (Baird et al, 2018;Castro & Liskov, 2002;Choi et al, 2018;Heaton, 2015;Maxmen, 2018;Swan, 2015) have provided an avenue to develop decision support systems that can allow human specialists in the AOCC to readily assess and validate the effectiveness of their decisions while concurrently recovering the airline network during irregular operations. To that effect, this paper provides a compendious discussion of the mechanisms that enable the integration of constituent AI models developed from previous work (Ogunsina & Okolo, 2021;Ogunsina et al, 2021b), which define the intelligent agent for each functional role (or domain) in the AOCC, and the interaction of multiple intelligent agents for simultaneously-integrated recovery (Castro et al, 2014) during airline disruption management.…”

Section: The Problemmentioning

confidence: 99%

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Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

Ogunsina¹,

DeLaurentis²

2021

Preprint

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Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the airline operations control center. To that effect, we provide a discussion and demonstration of an agnostic and systematic paradigm for enabling expeditious simultaneously-integrated recovery of all problem dimensions during This article represents one full chapter from the corresponding author's completed doctoral dissertation.

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Section: The Problemmentioning

confidence: 99%

“…For our demonstration, each of the eleven functional roles in SWA-NOC is modeled as a separate AI system (i.e. intelligent agent) defined by a uncertainty transfer function model (UTFM) and a predictive transfer function model (PTFM), as described by Ogunsina et al (2021b) and Ogunsina & Okolo (2021) respectively.…”

Section: A Case Studymentioning

confidence: 99%

Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

Ogunsina¹,

DeLaurentis²

2021

Preprint

Self Cite

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Modeling and prediction of flame retardant performance of alkyl phosphinate using artificial neural network and adaptive neural fuzzy inference system

Wang,

Lang,

Xia

et al. 2024

Polymers for Advanced Techs

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The flame retardancy of polymer materials is closely linked to the types and compositions of flame retardants used. However, due to the complex relationships involved, it is difficult to quantify and predict flame retardancy using linear equations and models. Alkyl phosphinate flame retardants have emerged as promising candidates for their environmentally friendly, low toxicity, and excellent flame retardant properties. In this study, we aimed to develop a novel, low‐cost alkyl phosphinate flame retardant by predicting the quantitative impact of flame retardant composition on flame retardancy. To establish prediction models for the limiting oxygen index (LOI) value of alkyl phosphinate flame retardants, we utilized artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) programs, namely ANN, ANFIS‐grid partition (GP), and ANFIS‐subtractive clustering (SC). The prediction models were trained and validated, and their reliability was assessed based on the root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE) criteria. Our results indicate that the ANFIS‐SC prediction model exhibits the highest reliability, with high accuracy and ideal prediction results.

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An Artificial Neural Network for Predicting Air Traffic Demand Based on Socio-Economic Parameters

Alam¹,

Deb

Amin³

et al. 2023

Preprint

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Artificial Neural Network Modeling for Airline Disruption Management

Cited by 4 publications

References 16 publications

Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

Enabling Integration and Interaction for Decentralized Artificial Intelligence in Airline Disruption Management

Modeling and prediction of flame retardant performance of alkyl phosphinate using artificial neural network and adaptive neural fuzzy inference system

An Artificial Neural Network for Predicting Air Traffic Demand Based on Socio-Economic Parameters

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