Genetic algorithms for calibrating airline revenue management simulations

Vock, Sebastian; Enz, Steffen; Cleophas, Catherine

doi:10.1109/wsc.2014.7019894

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…defect prediction [80] failure prediction [81] defect detection [82] Y: defect prediction [83] defect detection [84], [85] Safety and security Y: train protection [86], speed error reduction [87] Y: accidents [53] disruptions [88] Autonomous driving and control Y: energy optimization [89] intelligent train control [90] Y: intelligent train control [55] Traffic planning and management Y: train timetabling [91], [92] Y: delay analysis [40], train rescheduling [93] train timetabling [63], [94], train shunting [95] Revenue management P: revenue simulation [96] P: overall revenue management [97] inventory control and prediction [98] Transport policy P: energy network policy making [99] U Passenger mobility P: demand forecasting [100] Y: flow prediction [101], [102] and reinforcement learning for optimal train control. Reference [89] proposed a method for energy optimisation of the train movement applying control based on genetic algorithms.…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Bešinović

Donato

Flammini

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

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Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Bešinović

Donato

Flammini

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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HotelSimu: Simulation-Based Optimization for Hotel Dynamic Pricing

Mariello

Dalcastagné

Brunato

2020

Lecture Notes in Computer Science

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material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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The Influence of the eBusiness on the Airline Revenue Management

Engel

Karovič

2022

Studies in Systems, Decision and Control

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Genetic algorithms for calibrating airline revenue management simulations

Cited by 4 publications

References 22 publications

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

HotelSimu: Simulation-Based Optimization for Hotel Dynamic Pricing

The Influence of the eBusiness on the Airline Revenue Management

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