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Air Transport
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Contemporary Issues in Air Transport SeriesVerlag: Chantilly : Elsevier, 2025Copyright-Datum: ©2025Auflage: 1st edBeschreibung: 1 Online-Ressource (458 pages)ISBN:- 9780443451515
- 0443451516
- 9780443451508
- 629.1300285
- TL725.3
Inhalte:
PPN: PPN: 1970429259Package identifier: Produktsigel: BSZ-4-NLEBK-KAUB | ZDB-4-NLEBK
Front Cover -- Air Transport: A Computer Science Perspective -- Copyright -- Contents -- Preface -- List of acronyms -- I Foundations -- 1 Introduction -- 1.1 A brief overview on air transport -- Airline operations -- Airport operations -- Air traffic management and air traffic flow management -- 1.2 History of computer science applications -- The early automation era (1950s to 1970s) -- The decision support and optimization era (1980s to 1990s) -- The digital integration era (2000s to 2010s) -- The AI-driven and autonomous systems era (2020s to date) -- 1.3 The present challenges in air transport -- Challenge 1: reaching economic viability -- Challenge 2: reaching environmental sustainability -- Challenge 3: maintaining safety and security levels -- Challenge 4: incorporating future technological advances -- 1.4 Outline of this book -- References -- 2 Computational complexity -- 2.1 Overview -- 2.2 Knapsack problem -- 2.3 Subset sum problem -- 2.4 Vertex cover problem -- 2.5 Traveling salesman problem -- 2.6 Vehicle routing problem -- 2.7 Conclusions -- References -- 3 Computational intelligence -- 3.1 Overview -- 3.2 Search algorithms -- 3.2.1 Basic search methods -- 3.2.2 A* search algorithm -- 3.2.3 Monte Carlo tree search -- 3.3 Neural networks -- 3.3.1 Basic concepts -- 3.3.2 Backpropagation -- 3.3.3 Convolutional neural network -- 3.4 Genetic algorithms -- 3.5 Tabu search -- 3.6 Large language models -- 3.7 Conclusions -- References -- II Airline operations -- 4 Network design -- 4.1 Overview -- 4.2 Problem formulations -- 4.2.1 Uncapacitated single allocation p-hub median problem with complete hub network -- 4.2.2 Uncapacitated multiple allocation p-hub median problem with complete hub network -- 4.2.3 Uncapacitated single/multiple allocation p-hub median problem with incomplete hub network.
4.2.4 Capacitated single/multiple allocation p-hub median problem with complete hub network -- 4.2.5 Hub location problems with setup cost for hubs -- 4.3 State of the art -- 4.3.1 Literature on p-hub median problems -- 4.3.2 Literature on hub location problems with fixed costs -- 4.3.3 Literature on stratified HLPs -- 4.4 Datasets -- 4.5 Use cases -- 4.5.1 Code and data preparation -- 4.6 Conclusions -- References -- 5 Flight scheduling -- 5.1 Overview -- 5.2 Problem formulations -- 5.2.1 Passenger demand -- 5.2.2 Market share -- 5.2.3 Uncertainty -- 5.2.4 Non-incremental model -- 5.3 State of the art -- 5.3.1 Literature on FSP with market and demand -- 5.3.2 Literature on FSP with uncertainty -- 5.4 Datasets -- 5.5 Use cases -- 5.5.1 Code and data preparation -- 5.6 Conclusions -- References -- 6 Fleet assignment -- 6.1 Overview -- 6.2 Problem formulations -- 6.2.1 Basic connection network model -- 6.2.2 Basic time-space network model -- 6.2.3 String-based model -- 6.2.4 Itinerary-based model -- 6.2.5 Robust model -- 6.3 State of the art -- 6.3.1 Model development -- 6.3.2 Various operational considerations -- 6.3.3 Robustness -- 6.4 Datasets -- 6.5 Use case -- 6.5.1 Code and data preparation -- 6.6 Conclusions -- References -- 7 Aircraft routing -- 7.1 Overview -- 7.2 Problem formulations -- 7.2.1 Maintenance routing -- Time-space network -- Connection network -- Route network -- 7.2.2 Location routing -- 7.3 Literature review -- 7.3.1 Network structure -- 7.3.2 Algorithms -- 7.4 Datasets -- 7.5 Use cases -- 7.5.1 Code and data preparation -- 7.6 Conclusions -- References -- 8 Crew scheduling -- 8.1 Overview -- 8.2 Problem formulations -- 8.2.1 Cockpit crew pairing problem -- 8.2.2 Cabin crew pairing problem -- 8.2.3 Crew rostering problem -- 8.3 State of the art -- 8.3.1 Sequential airline crew scheduling -- 8.3.2 Airline crew recovery.
8.3.3 Integrated airline crew scheduling -- 8.4 Datasets -- 8.4.1 G1422 dataset -- 8.4.2 Simulator dataset -- 8.5 Use cases -- 8.5.1 Code and data preparation -- 8.6 Conclusions -- References -- III Airport operations -- 9 Gate assignment -- 9.1 Overview -- 9.2 Problem formulations -- 9.3 State of the art -- 9.4 Datasets -- 9.5 Use cases -- 9.5.1 Code and data preparation -- 9.6 Conclusions -- References -- 10 Ground vehicle scheduling -- 10.1 Overview -- 10.2 Problem formulations -- 10.2.1 GVS with one trip per vehicle and ground-centric cost -- 10.2.2 GVS with multiple trips per vehicle and flight-centric cost -- 10.3 State of the art -- 10.3.1 Literature on single vehicle scheduling -- 10.3.2 Literature on heterogeneous vehicle scheduling -- 10.3.3 Literature on GVS recent research directions -- 10.4 Datasets -- 10.5 Use cases -- 10.5.1 Code and data preparation -- 10.6 Conclusions -- References -- 11 Delay prediction -- 11.1 Overview -- 11.2 Problem formulations -- 11.2.1 Regression problem -- 11.2.2 Classification problems -- 11.2.3 Traditional statistical models -- 11.2.4 Machine learning models -- 11.2.5 Deep learning models -- 11.2.6 Hybrid models -- 11.3 State of the art -- 11.3.1 Literature on traditional statistical models -- 11.3.2 Literature on machine learning models -- 11.3.3 Literature on deep learning models -- 11.3.4 Literature on hybrid models -- 11.4 Datasets -- 11.5 Use cases -- 11.5.1 Code and data preparation -- 11.6 Conclusions -- References -- IV Air traffic flow management -- 12 Trajectory optimization -- 12.1 Overview -- 12.2 Problem formulations -- 12.2.1 Kinematic optimization -- Takeoff -- Climb -- Cruise -- Descent -- Landing -- 12.2.2 Dynamics optimization -- 12.2.3 Model-based optimization -- Multi-objective optimization -- Optimization under uncertainty -- 12.3 State of the art.
12.3.1 Literature on problem formulations -- Kinematic optimization -- Dynamics optimization -- 12.3.2 Literature on modeling -- Multi-objective optimization -- Uncertainty-based optimization -- 12.3.3 Literature on solution methods -- 12.4 Datasets -- 12.4.1 ADS-B -- 12.4.2 Weather dataset -- 12.4.3 Aircraft performance data -- 12.5 Use cases -- 12.5.1 Code and data preparation -- 12.6 Conclusions -- References -- 13 Airspace sectorization -- 13.1 Overview -- 13.2 Problem formulations -- 13.2.1 Static airspace sectorization -- 13.2.2 Dynamic airspace configuration -- Group-based mathematical model -- Configuration-based mathematical model -- 13.3 State of the art -- 13.3.1 Literature on geometry-based sectorization -- 13.3.2 Literature on cell-based sectorization -- 13.3.3 Literature on graph-based sectorization -- 13.3.4 Literature on future sectorization trends -- 13.4 Datasets -- 13.5 Use cases -- 13.5.1 Code and data preparation -- 13.6 Conclusions -- References -- 14 Network resilience assessment -- 14.1 Overview -- 14.2 Problem formulations -- 14.2.1 Node importance -- 14.2.2 Link importance -- 14.3 State of the art -- 14.4 Datasets -- 14.5 Use cases -- 14.5.1 Code and data preparation -- 14.6 Conclusions -- References -- V Conclusions -- 15 Summary and outlook -- 15.1 Biometric/digital identity systems (now-15 years) -- 15.2 Artificial intelligence and machine learning (now-25 years) -- 15.3 Autonomous systems (now-30 years) -- 15.4 Blockchain applications (5-25 years) -- 15.5 Digital twins (5-25 years) -- 15.6 Human-machine interfaces (10-50 years) -- 15.7 Quantum computing for optimization (20-50 years) -- References -- Index -- Back Cover.
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