Airline Schedule Planning: Accomplishments and Opportunities

Barnhart, Cynthia; Cohn, Amy M.

doi:10.1287/msom.1030.0018

Cited by 102 publications

(42 citation statements)

References 51 publications

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“…Unlike most of the studies mentioned above, which focus on specific airports and shorter time periods, our model uses data on all the domestic flights by all major airlines flown in the United States during 2005 through 2007 consisting of more than 21 million observations. This study contributes to the robust airline scheduling literature (for a review, see Ball et al 2007 andCohn 2004) by developing a stochastic model of delay propagation and using publicly available industrywide data. Researchers have conducted several studies regarding modeling flight delay propagation.…”

Section: Literature Reviewmentioning

confidence: 99%

Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Arikan

Deshpande

Sohoni

2013

Operations Research

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Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines' on-time performance at its worst level in 2007 since 1995. A recent report by the Joint Economic Committee of the U.S. Congress chaired by Senator Charles E. Schumer has estimated that the total cost to the U.S. economy because of flight delays was as much as $41 billion in 2007. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which are the bottleneck airports in the U.S. air-travel infrastructure (i.e., airports that cause most delay propagation)? How would increasing airport capacity at these airports alleviate delay propagation? What are the appropriate metrics for measuring the robustness of airline schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight (and, hence, deserves managerial attention)? Flight delays are typically attributed to two factors: (i) the randomness in the intrinsic travel time for a scheduled flight (which is the travel time excluding propagated delays), and (ii) the propagation of this randomness through the air-travel network and infrastructure. We model both of these factors that cause travel delays. The contribution of this paper is twofold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Our stochastic models allow us to develop three important robustness measures for airline networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure, which, if addressed, could lead to a significant improvement in air-travel reliability.

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Section: Literature Reviewmentioning

confidence: 99%

Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Arikan

Deshpande

Sohoni

2013

Operations Research

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“…In constructing each new schedule, changes are introduced to the existing schedule to reflect changes in demands and the market environment. Because of the enormous size and complexity of the problem, schedule planning is a multi-step process, usually separated into four, sequentially solved subproblems: schedule design, fleet assignment, maintenance routing, and crew scheduling (Barnhart and Cohn 2004;Barnhart and Vaze 2015).…”

Section: Schedule Planningmentioning

confidence: 99%

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

Cadarso

Vaze

Barnhart

et al. 2017

Transportation Science

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Abstract. Airlines and high speed rail are increasingly competing for passengers, especially in Europe and Asia. Competition between them affects the number of captured passengers and, therefore, revenues. We consider competition between airlines (legacy and low-cost) and high speed rail. We develop a new approach that generates airline schedules using an integrated mixed integer, nonlinear optimization model that captures the impacts of airlines' decisions on passenger demand. We estimate the demand associated with a given schedule using a nested logit model. We report our computational results on realistic problem instances of the Spanish airline IBERIA and show that the actual airline schedules are found to be reasonably close to the schedules generated by our approach. Next, we use this optimization modeling approach under multimodal competition to evaluate multiple scenarios involving entry of high speed rail into new markets. We account for the possibility of demand stimulation as a result of the new services. We validate our approach using data from markets that had an entry by high speed rail in the past. The out-of-sample validation results show a close match between the predicted and observed solutions. Finally, we use our validated model to predict the impacts of future entry by high speed rail in new markets. Our results provide several interesting and useful insights into the schedule changes, fleet composition changes, and fare changes that will help the airline cope effectively with the entry of high speed rail.

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“…In the classic approach, the problems that occur in each step of the planning phase are separately implemented or the problems in a number of steps are integrated and solved simultaneously. Details on this approach can be found in [4], [5], and [6]. In the robust approach, the problems occurring in one or more steps of the planning phase are solved in relation to the operational phase.…”

Section: Introductionmentioning

confidence: 99%

Airline delay prediction by machine learning algorithms

Khaksar

Sheikholeslami

2017

Scientia Iranica

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Flight planning, as one of the challenging issue in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through proposed approaches that are based on machine learning algorithms. Parameters that enable the effective estimation of delay are identified, after which Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method are applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a U.S. flight dataset and then refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks are visibility, wind, and departure time, whereas those affecting delay in Iranian airline flights are fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurance and magnitude in both the whole-network US and Iranian. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.

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Airline Schedule Planning: Accomplishments and Opportunities

Cited by 102 publications

References 51 publications

Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

Airline delay prediction by machine learning algorithms

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