Measuring the Robustness of Airline Fleet Schedules

Bian, F.; Burke, Edmund K.; Jain, S.; Kendall, Graham; Koole, GM; Landa-Silva, Dario; Mulder, Jeroen; Paelinck, Marc; Reeves, Colin R.; Rusdi, Ibnu; Suleman, MO

doi:10.1007/0-387-27744-7_19

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…Their complexity varies according to the number of constraints addressed. Typical scheduling problems in the airport domain are: aircraft scheduling (Bian, Burke, Jain & Kendall, 2005;El Moudani & Mora-Camino, 2000;Gurtner, Bongiorno, Ducci & Miccichè, 2016;Yan & Chen, 2008), ground crew scheduling (Clausen, 2011), disruption management (Pereira, Fadigas, Senna & Moret, 2011) (Løve, Sørensen, Larsen & Clausen, 2002), aircraft landing sequence scheduling (García Ansola, García Higuera, Pastor & Otamendi, 2011; Tavakkoli-Moghaddam, Yaghoubi-Panah & Radmehr, 2012) or personnel training scheduling (Brucker, Qu & Burke, 2011). Particularly in the domain of personnel scheduling, the most popular type of scheduling problem is the aircraft crew scheduling, while the problem of ground crew has mainly been neglected.…”

Section: Discussionmentioning

confidence: 99%

Digital Coaching to Build Sustainable Wellness Routines for Young Elderly

Carlsson¹,

Walden²

2017

Digital Transformation – From Connecting Things to Transforming Our Lives

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This paper focuses on the design and development of a noninvasive smart and pervasive mobile solution to measure blood glucose without the need for drawing blood or pricking fingers. Specifically, it examines the possibility of sensors using Terahertz (THz) technology to measure blood glucose. This paper reports on a research in progress looking at identifying and then designing superior strategies for measuring blood glucose. It presents the central role that measuring blood glucose plays in diabetes care management. It then highlights the current methods and problems and concerns with finger pricking. From there, the paper proffers a non-invasive solution using THz technology to measure blood glucose and outlines the approach to design and develop such a solution using a decision science methodology.

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Section: Discussionmentioning

confidence: 99%

Digital Coaching to Build Sustainable Wellness Routines for Young Elderly

Carlsson¹,

Walden²

2017

Digital Transformation – From Connecting Things to Transforming Our Lives

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“…Several contributions in the literature attempt to model an uncertainty set, see for example Lan et al (2006), Shebalov and Klabjan (2006), Policella (2004). The main conclusions of the works adopting robust approaches are that the obtained solutions exhibit a particular property such as the number of plane crossings (Klabjan et al (2002), Bian et al (2005)), a reduced length of plane rotations (Rosenberger et al, 2004) or increased idle time (Al-Fawzana and Haouari, 2005). Remarkably, models aiming at an increase of the solution's recoverability draw the same conclusion: the stochastic model with recourse of Yen and Brige (2006) addresses the crew scheduling problem.…”

Section: Introductionmentioning

confidence: 94%

Uncertainty feature optimization: An implicit paradigm for problems with noisy data

2011

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Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise.In this paper, we introduce a framework that considers the uncertain data implicitly. We define the concept of Uncertainty Features (UF), which are problem-specific structural properties of a solution. We show how to formulate an uncertain problem using the Uncertainty Feature Optimization (UFO) framework as a multi-objective problem. We show that stochastic programming and robust optimization are particular cases of the UFO framework. We present computational results for the Multi-Dimensional Knapsack Problem (MDKP) and discuss the application of the framework to the airline scheduling problem.

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“…Due to increased air traffic, airports as well as airlines are facing diverse challenges in ensuring efficient, high quality services while generating profit [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Simulation Based Optimisation of Ground Crews: Case of a Regional Airport

Rodič¹,

Баггиа

2018

2018 UBT International Conference

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Paper presents the simulation models built within an airport ground crew scheduling automatization project at a regional airport. Our goal was to develop robust ground crew task scheduling and shift generation algorithms that would improve on existing heuristic rules. We have utilized simulation modeling to develop and validate the algorithms, starting with a model of the existing scheduling process coded and visualized in spreadsheet software and ending with a hybrid Discrete Event and Agent Based model used for the visualization and verification of the optimized processes. Explicit and tacit expert knowledge was recorded through meetings with airport personnel managers and observation of the ground crew processes. Gathered knowledge was combined with business rules, contractual limitations and labor legislation to develop the final version of the algorithms.

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Measuring the Robustness of Airline Fleet Schedules

Cited by 12 publications

References 23 publications

Digital Coaching to Build Sustainable Wellness Routines for Young Elderly

Digital Coaching to Build Sustainable Wellness Routines for Young Elderly

Uncertainty feature optimization: An implicit paradigm for problems with noisy data

Simulation Based Optimisation of Ground Crews: Case of a Regional Airport

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