Scheduling of airport runway operations using stochastic branch and bound methods

•We present a solution methodology for the stochastic runway scheduling problem.•The stochastic branch and bound algorithm is used find optimal/good sequences.•A method to dynamically update the sample sizes for bound estimation is proposed.•Algorithm gives solutions with 5–7% shorter makespan than...

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Published in:	Transportation research. Part C, Emerging technologies Vol. 45; pp. 119 - 137
Main Authors:	Sölveling, Gustaf, Clarke, John-Paul
Format:	Journal Article
Language:	English
Published:	Elsevier India Pvt Ltd 01.08.2014
Subjects:	Aircraft Airport runways Algorithms Computation Machine scheduling Mathematical models Optimization Scheduling Stochastic aircraft scheduling Stochastic branch-and-bound algorithm Stochastic programming Stochasticity Machine scheduling Stochastic branch-and-bound algorithm Stochastic aircraft scheduling Stochastic programming
ISSN:	0968-090X, 1879-2359
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Abstract	•We present a solution methodology for the stochastic runway scheduling problem.•The stochastic branch and bound algorithm is used find optimal/good sequences.•A method to dynamically update the sample sizes for bound estimation is proposed.•Algorithm gives solutions with 5–7% shorter makespan than deterministic model. In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochastic branch and bound algorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochastic branch and bound algorithm have, on average, 5–7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1min of computation time.
AbstractList	•We present a solution methodology for the stochastic runway scheduling problem.•The stochastic branch and bound algorithm is used find optimal/good sequences.•A method to dynamically update the sample sizes for bound estimation is proposed.•Algorithm gives solutions with 5–7% shorter makespan than deterministic model. In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochastic branch and bound algorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochastic branch and bound algorithm have, on average, 5–7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1min of computation time. In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochastic branch and bound algorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochastic branch and bound algorithm have, on average, 5-7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1min of computation time.
Author	Sölveling, Gustaf Clarke, John-Paul
Author_xml	– sequence: 1 givenname: Gustaf surname: Sölveling fullname: Sölveling, Gustaf email: gustaf.solveling@gmail.com organization: School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA 30332, USA – sequence: 2 givenname: John-Paul surname: Clarke fullname: Clarke, John-Paul organization: School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Drive, Atlanta, GA 30332, USA
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Snippet	•We present a solution methodology for the stochastic runway scheduling problem.•The stochastic branch and bound algorithm is used find optimal/good... In this paper we present a solution methodology based on the stochastic branch and bound algorithm to find optimal, or close to optimal, solutions to the...
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SubjectTerms	Aircraft Airport runways Algorithms Computation Machine scheduling Mathematical models Optimization Scheduling Stochastic aircraft scheduling Stochastic branch-and-bound algorithm Stochastic programming Stochasticity
Title	Scheduling of airport runway operations using stochastic branch and bound methods
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