A Decision-Support Tool for the Integrated Airline Recovery Using a Machine Learning Resources Selection Approach
A Decision-Support Tool for the Integrated Airline Recovery Using a Machine Learning Resources Selection Approach

Author: Berend Eikelenboom

Publisher:

Published: 2023

Total Pages: 0

ISBN-13:

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Airlines frequently deal with unexpected disruptions, which must be resolved to resume operations. Decision-support tools help airlines with disruption management. However, the computation of an integrated recovery solution considering aircraft, crew, and passengers at the same time is a hard problem to solve, requiring long computational times to compute globally optimal solutions. On the other hand, faster sequential approaches have been proposed, which recover one resource at a time, but these compromise the quality of the recovery solution. This paper presents a real-time decision-support approach to solve the integrated airline recovery problem. The model simultaneously recovers the schedule and allocates aircraft and cockpit crew pairs to the flights while minimizing additional incurred costs. Passengers are implicitly recovered by considering missed connections and prescribing alternative itineraries. Recovery actions include delaying and canceling flights, swapping aircraft, swapping and deadheading crew, or using reserve crew. A machine-learning ranking algorithm reduces the problem's computational complexity by selecting a subset of the resources that are likely to be involved in the recovery plan, such that only part of the network is considered. The decision-support tool was evaluated on disruption scenarios of one of the largest airlines in the world: Delta Airlines. The results show that the machine learning selection reduces the average computational time 15-fold compared to the integrated recovery model that uses the complete network. Good quality aircraft and crew recovery solutions could be computed in under two minutes for 98% of the disruption cases tested.

Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare

Author: Adam Bohr

Publisher: Academic Press

Published: 2020-06-21

Total Pages: 385

ISBN-13: 0128184396

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Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Disruption Management
Disruption Management

Author: Gang Yu

Publisher: World Scientific

Published: 2004

Total Pages: 313

ISBN-13: 9812561706

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This pioneering book addresses the latest research findings and application results on disruption management, which is the study of how to dynamically recover a predetermined operational plan when various disruptions prevent the original plan from being executed smoothly.

Column Generation
Column Generation

Author: Guy Desaulniers

Publisher: Springer Science & Business Media

Published: 2006-03-20

Total Pages: 369

ISBN-13: 0387254862

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Column Generation is an insightful overview of the state of the art in integer programming column generation and its many applications. The volume begins with "A Primer in Column Generation" which outlines the theory and ideas necessary to solve large-scale practical problems, illustrated with a variety of examples. Other chapters follow this introduction on "Shortest Path Problems with Resource Constraints," "Vehicle Routing Problem with Time Window," "Branch-and-Price Heuristics," "Cutting Stock Problems," each dealing with methodological aspects of the field. Three chapters deal with transportation applications: "Large-scale Models in the Airline Industry," "Robust Inventory Ship Routing by Column Generation," and "Ship Scheduling with Recurring Visits and Visit Separation Requirements." Production is the focus of another three chapters: "Combining Column Generation and Lagrangian Relaxation," "Dantzig-Wolfe Decomposition for Job Shop Scheduling," and "Applying Column Generation to Machine Scheduling." The final chapter by François Vanderbeck, "Implementing Mixed Integer Column Generation," reviews how to set-up the Dantzig-Wolfe reformulation, adapt standard MIP techniques to the column generation context (branching, preprocessing, primal heuristics), and deal with specific column generation issues (initialization, stabilization, column management strategies).

Analytics, Data Science, and Artificial Intelligence
Analytics, Data Science, and Artificial Intelligence

Author: Ramesh Sharda

Publisher:

Published: 2020-03-06

Total Pages: 832

ISBN-13: 9781292341552

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For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

Integrated Optimization Model for Airline Schedule Design
Integrated Optimization Model for Airline Schedule Design

Author: Flora A. Garcia

Publisher:

Published: 2004

Total Pages: 100

ISBN-13:

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The purpose of the National Airspace System Strategy Simulator is to provide the FAA with a decision support system to evaluate long-term infrastructure and regulatory strategies. The NAS strategy simulator consists of several modules representing the different entities within the NAS embedded in a system dynamics framework. The MIT Airline Scheduling Module is the module within the NAS Strategy Simulator that represents the decision making process of the airlines with respect to the schedules that they fly. The MIT Airline Scheduling Module is an incremental optimization tool to determine schedule changes from one time step to another that best meets demand using available resources. The optimization model combines an Integrated Schedule Design and Fleet Assignment model and a model, based on Passenger Decision Window model, that determines passenger preference for itineraries. We simultaneously establish frequency, departure times, fleet assignment, passenger loads and revenue within a competitive environment. Optimization methods often lead to extreme schedule decisions such as eliminating service to markets, often small markets, that are not financially profitable for the airlines. This is of grave concern to government policy makers as rural access to markets, goods and services is a politically charged subject. The issue is to understand what is likely to happen in small communities if the government doesn't respond in some way and how much subsidy, if any, would it be necessary to encourage airlines to maintain service in these markets. The approach we will use is based on economic policy and cost-benefit analysis.