Academic literature on the topic 'Airline Schedule'
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Journal articles on the topic "Airline Schedule"
Massoud, Bazargan, and Xiaoxu Chen. "Airline Hangars Balanced Manpower Utilization." International Journal of Aviation Systems, Operations and Training 3, no. 1 (January 2016): 49–58. http://dx.doi.org/10.4018/ijasot.2016010104.
Full textZagrajek, Paweł, and Adam Hoszman. "Impact of Ground Handling on Air Traffic Volatility." Journal of Management and Financial Sciences, no. 33 (July 27, 2019): 147–55. http://dx.doi.org/10.33119/jmfs.2018.33.8.
Full textYimga, Jules. "Competition and Schedule Padding in the US Airline Industry." Review of Network Economics 20, no. 1 (March 1, 2021): 1–33. http://dx.doi.org/10.1515/rne-2021-0016.
Full textMunoz, Claudia, Henry Laniado, and Jorge Córdoba. "Airline choice model for an international round-trip flight considering outbound and return flight schedules." Archives of Transport 54, no. 2 (June 30, 2020): 75–93. http://dx.doi.org/10.5604/01.3001.0014.2969.
Full textBrueckner, Jan K., and Ricardo Flores-Fillol. "Airline Schedule Competition." Review of Industrial Organization 30, no. 3 (August 18, 2007): 161–77. http://dx.doi.org/10.1007/s11151-007-9140-1.
Full textLaw, Colin. "The study of customer relationship management in Thai airline industry: A case of Thai travelers in Thailand." Journal of Airline and Airport Management 7, no. 1 (February 17, 2017): 13. http://dx.doi.org/10.3926/jairm.86.
Full textCadarsoa, Luis, and Ángel Marín. "Integrated Robust Airline Schedule Development." Procedia - Social and Behavioral Sciences 20 (2011): 1041–50. http://dx.doi.org/10.1016/j.sbspro.2011.08.113.
Full textMhlanga, Oswald, Jacobus Steyn, and John Spencer. "The airline industry in South Africa: drivers of operational efficiency and impacts." Tourism Review 73, no. 3 (August 20, 2018): 389–400. http://dx.doi.org/10.1108/tr-07-2017-0111.
Full textTao, Mei, Lan Ma, and Yiming Ma. "Flight schedule adjustment for hub airports using multi-objective optimization." Journal of Intelligent Systems 30, no. 1 (January 1, 2021): 931–46. http://dx.doi.org/10.1515/jisys-2020-0114.
Full textLagos, Carlos, Felipe Delgado, and Mathias A. Klapp. "Dynamic Optimization for Airline Maintenance Operations." Transportation Science 54, no. 4 (July 2020): 998–1015. http://dx.doi.org/10.1287/trsc.2020.0984.
Full textDissertations / Theses on the topic "Airline Schedule"
Lohatepanont, Manoj 1974. "Incremental airline schedule design." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/28210.
Full textIncludes bibliographical references (p. 83-86).
We consider the problem of integrating flight schedule design and fleet assignment decisions at airlines. The flight schedule design problem involves selecting and scheduling the set of flight legs that an airline will include in its service network. Fleet assignment involves assigning a particular aircraft type to each flight leg in the schedule. Due to the particularly challenging nature of schedule design problems, we limit our focus to that of incremental schedule design. Incremental schedule design involves the modification of a given flight schedule to produce an improved schedule by adding, deleting, and rescheduling flight legs. We present models and algorithms to achieve incremental schedule design and unlike previous schedule design efforts, we explicitly model flight demand and supply interactions. We present two case studies, using our models and algorithms. The first case study allows flight additions and deletions, while the second allows flights to be rescheduled. Future case studies well integrate these flight modification options. In our first case study, high-yield flights are maintained in the schedule and low-yield flights are dropped. Although the resulting schedule incurs higher spill costs, the savings from flight operating costs are sufficiently large to offset these higher spill costs, resulting in a more profitable schedule. The second case study, allowing flights to be rescheduled, considers several network sizes including the domestic network of a large U.S. airline. We consider Free Flight, a system allowing reduced flying times due to improved utilization of the national airspace. We find that reductions in flying times of about 10% can lead to dramatic cost savings for the airline, including reductions in the number of aircraft needed to fly the flight schedule.
by Manoj Lohatepanont.
S.M.
Al-Haimi, Abdullah A. "Airline schedule punctuality management." Thesis, Cranfield University, 1991. http://dspace.lib.cranfield.ac.uk/handle/1826/9828.
Full textJiang, Hai 1979. "Dynamic airline scheduling and robust airline schedule de-peaking." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37976.
Full textIncludes bibliographical references (p. 151-156).
Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights have empty seats at departure, while others suffer a lack of seats to accommodate passengers who desire to travel. Recognizing that demand forecast quality for a particular departure date improves as the date comes close, we tackle this challenge by developing a dynamic scheduling approach that re-optimizes elements of the flight schedule during the passenger booking period. The goal is to match capacity to demand, given the many operational constraints that restrict possible assignments. We introduce flight re-timing as a dynamic scheduling mechanism and develop a re-optimization model that combines both flight re-timing and flight re-fleeting. Our re-optimization approach, re-designing the flight schedule at regular intervals, utilizes information from both revealed booking data and improved forecasts available at later re-optimizations. Experiments are conducted using data from a major U.S. airline. We demonstrate that significant potential profitability improvements are achievable using this approach.
(cont.) We complement this dynamic re-optimization approach with models and algorithms to de-peak existing hub-and-spoke flight schedules so as to maximize future dynamic scheduling capabilities. In our robust de-peaking approach, we begin by solving a basic de-peaking model to provide a basis for comparison of the robust de-peaked schedule we later generate. We then present our robust de-peaking model to produce a schedule that maximizes the weighted sum of potentially connecting itineraries and attains at least the same profitability as the schedule produced by the basic de-peaking model. We provide several reformulations of the robust de-peaking model and analyze their properties. To address the tractability issue, we construct a restricted model through an approximate treatment of the profitability requirement. The restricted model is solved by a decomposition based solution approach involving a variable reduction technique and a new form of column generation. We demonstrate, through experiments using data from a major U.S. airline, that the schedule generated by our robust de-peaking approach achieves improved profitability.
by Hai Jiang.
Ph.D.
Morin, Massimo (Massimo Giacomo) 1971. "Metrics and methods of improving airline schedule reliability." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8097.
Full textIncludes bibliographical references (p. 161-163).
Airline scheduling is a daunting task. Much time and resources are spent by airlines developing a schedule that meets expectations of profitability and competitiveness. Most of the time, however, the reliability aspect has a minor, if any, role in such a process. In reality disruption of the schedule occurs due to unforeseen events such as weather conditions, traffic congestion, and mechanical problems. The outcomes of these events are cancellations and delays. The impact that these disruptions have on airline operations is not only the increased cost for system maintenance and recovery, but also the loss of profitability and the perception of poor and unreliable service for the flying customer. In this thesis we present an analysis of the schedule design process, highlight the drawbacks of the current proceedings and outline of new and more flexible framework for schedule design. We define a reliability measure, the Option Value, and a way of comparing flights based on the reliability they are providing, via the Option Disruption Value. The idea of reliability is based on the concept of flight performance: a flight is more reliable if it is able to match or outperform the on-time performance of the flights that leaves its origin station and arrives at its final destination at or near its arrival and departure times. Based on these two measurements, we quantify the robustness and coverage of a sample schedule. Alternative passenger ratings are defined based on the concept of alternative itineraries (Coverage) and alternative independent itineraries (Robustness) that connect two locations. These are the Flight Options and the Flight Protection Options, respectively. Fifteen methods to modify flight schedule are proposed. One method, Reduce/increase Flight Slack Time (R/IFTS) was evaluated. Results indicate that R/IFTS was effective in increasing reliability in 70% of the flight considered, but that other methods need to be employed if reliability is to be increased further.
by Massimo Morin.
S.M.
Shenoi, Rajesh Gopalakrishna. "Integrated airline schedule optimization : models and solution methods." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10655.
Full textIncludes bibliographical references (p. 133-148).
by Rajesh Gopalakrishna Shenoi.
Ph.D.
Yuan, Duojia, and S3024047@student rmit edu au. "Flight Delay-Cost Simulation Analysis and Airline Schedule Optimization." RMIT University. Aerospace, Mechanical, Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080618.092923.
Full textKarow, Michelle J. (Michael Janine) 1979. "Virtual hubs : an airline schedule recovery concept and model." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29572.
Full textIncludes bibliographical references (leaves 61-62).
Inclement weather at an airline's hub airport can be devastating to that airline's schedule. The repercussions resonate throughout the airline's network as capacity is reduced, connections are missed, and passengers are delayed on a larger scale than during irregular operations at a spoke airport. The main hypothesis behind the work presented in this thesis is that by shifting a small fraction of a connecting bank to strategically located, under-utilized airports during irregular operations, an airline can reduce costs and aircraft delays relative to current industry rescheduling practices. These proposed "virtual hubs" would, in addition to hosting selected connecting traffic that is shifted from the original hub in order to maximize passenger flow through the network, also reduce the demand on the nominal hub airport. The primary goal of this research project was to develop methods for the implementation of a virtual hub network and evaluate the potential benefits to the airline industry. To that end, a mathematical formulation is presented along with a case study of the benefits of a virtual hub to a major US airline. The actual recovered schedule and delay statistics for a day of irregular operations was compared to the results from the virtual hub network. Results indicate that significant passenger delays are reduced 94% and flight cancellations are reduced by 15% when a virtual hub network is implemented.
by Michelle J. Karow.
S.M.
Skaltsas, Gerasimos. "Analysis of airline schedule padding on U.S. domestic routes." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66870.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 125-127).
Every airline passenger faces the risk of arriving late because flight times are subjected to many sources of variability. These can be weather conditions and airspace congestion, imbalances between airport demand and capacity, fleet and crew availability, technical failures and delays in maintenance, and other airline operations such as boarding and fueling. The main objective of this thesis is to explore the most common sources of variability in flight operations and study how U.S. carriers add buffer time (or pad) to scheduled block time to account for them. Using flight data from FAA Aviation System Performance Metrics, we analyze the scheduled and actual flight times on 2359 directional non-stop domestic routes during 2009. The time of each flight is decomposed to delay at gate, taxi-out time, airborne time and taxi-in time. Then, the buffer time of each flight is computed, using as nominal airborne time the lO percentile of the actual airborne time distribution. Our study consists of two parts. First, an aggregate statistical analysis is performed, concentrating on trends and correlations among factors such as buffer, flight time components, route distance, seasonality effects, delays caused by Ground Delay Programs, time of day and day of week, a flight's relative position to other flights operated on the same day by the same aircraft, total number of flights operated by the same aircraft during a day, the role of airport and carriers' network structure. Finally, we perform an econometric analysis through linear regression models to estimate how some of the above factors affect carriers' padding and their on-time performance. The results indicate distance and time of day to be the most important factors that affect schedule padding. While absolute buffer increases with distance, when buffer is measured as a fraction of nominal block time it decreases exponentially. Furthermore, buffer and on-time performance fluctuate strongly over the course of the day, with flights scheduled to arrive during the evening peak having the worst on-time performance, despite the fact that these flights are padded the most. The data reveal that among the studied carriers Southwest pads its schedule more extensively, achieving a very high on-time performance, whereas other low cost carriers pad their flights substantially less, and have a lower on-time performance. Our findings also show that flights destined to the carrier's hub have more buffer than flights destined to spoke airports. Last, competition has a positive effect on schedule buffer and on-time performance.
by Gerasimos Skaltsas.
S.M.in Transportation
Agbokou, Claudine Biova 1979. "Robust airline schedule planning : review and development of optimization approaches." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30143.
Full textIncludes bibliographical references (p. 87-89).
Major airlines aim to generate schedules that maximize profit potential and satisfy constraints involving flight schedule design, fleet assignment, aircraft maintenance routing and crew scheduling. Almost all aircraft and crew schedule optimization models assume that flights, aircraft, crews, and passengers operate as planned. Thus, airlines typically construct plans that maximize revenue or minimize cost based on the assumption that every flight departs and arrives as planned. Because flight delays and cancellations result from numerous causes, including severe weather conditions, unexpected aircraft and crew failures, and congestion at the airport and in the airspace, this deterministic, optimistic scenario rarely, if ever, occurs. In fact, schedule plans are frequently disrupted and airlines often incur significant costs in addition to those originally planned. To address this issue, an approach is to design schedules that are robust to schedule disruptions and attempt to minimize realized, and not planned, costs. In this research, we review recovery approaches and robustness criteria in the context of airline schedule planning. We suggest new approaches for designing fleet assignments that facilitate recovery operations, and we present models to generate plans that allow for more robust crew operations, based on the idea of critical crew connections. We also examine the impact on robustness of new scheduling practices to debank hub airports.
by Claudine Biova Agbokou.
S.M.
Lohatepanont, Manoj 1974. "Airline fleet assignment and schedule design : integrated models and algorithms." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8297.
Full textIncludes bibliographical references (p. 187-192).
In scheduled passenger air transportation, airline profitability is critically influenced by the airline's ability to construct flight schedules containing flights at desirable times in profitable markets. In this dissertation, we study two elements of the schedule generation process, namely, schedule design and fleet assignment. The schedule design problem involves selecting an optimal set of flight legs to be included in the schedule, while the fleet assignment problem involves assigning aircraft types (or fleets) to flight legs to maximize revenues and minimize operating costs simultaneously. With the fleet assignment problem, we investigate the issues of network effects, spill, and recapture. On a constrained flight leg in which demand exceeds capacity, some passengers are not accommodated, or spilled. When passengers travel on two or more constrained legs, flight leg interdependencies or network effects arise because spill can occur on any of these legs. In most basic fleet assignment models, simplistic modeling of network effects and recapture leads to sometimes severe, miscalculations of revenues. Recapture occurs when some of the spilled passengers are re-accommodated on alternate itineraries in the system. In this dissertation, we develop new fleet assignment models that capture network effects, spill, and recapture. Another benefit of one of our models is its tractability and potential for further integration with other schedule planning steps.
(cont.) Our study shows that the benefits of modeling these elements can be as large as $100 million annually for a major U.S. airline. In addition, we show that modeling flight leg interdependence is more important than demand stochasticity for hub-and-spoke fleet assignment problems. We develop two models for schedule design, one assuming that the market share of an airline remains constant with schedule changes; and the other assuming that market share varies with schedule changes. The constant market share model, while less precise in its modeling, is much easier to solve than the variable market share model. We estimate that the potential benefits of these models range from $100 to $350 million annually.
Manoj Lohatepanont.
Sc.D.
Books on the topic "Airline Schedule"
Airline operations and delay management: Insights from airline economics, networks, and strategic schedule planning. Farnham, Surrey: Ashgate, 2010.
Find full textWu, Cheng-Lung. Airline operations and delay management: Insights from airline economics, networks, and strategic schedule planning. Farnham, Surrey: Ashgate, 2009.
Find full textRupp, Nicholas G. Airline schedule recovery after airport closures: Empirical evidence since September 11th. Cambridge, MA: National Bureau of Economic Research, 2003.
Find full textAir Canada's domestic economy fare formula and its relationship to average domestic scheduled costs. Ottawa-Hull: Canadian Transport Commission, 1986.
Find full textCivil Aviation Authority. Financial protection for scheduled airlines' passengers: Advice to the Secretaryof State for Transport. London: Civil Aviation Authority, 1991.
Find full textHartle, S. Is liberalisation occurring?: If so, what are the effects on scheduled airlines marketing strategies?. Oxford: Oxford Brookes University, 1996.
Find full textOffice, General Accounting. Military airlift: C-17 faces schedule, cost, and performance challenges : report to Congressional committees. Washington, D.C: The Office, 1989.
Find full textOffice, General Accounting. Military airlift: C-17 faces schedule, cost, and performance challenges : report to Congressional committees. Washington, D.C: The Office, 1989.
Find full textOffice, General Accounting. Military airlift: C-17 faces schedule, cost, and performance challenges : report to Congressional committees. Washington, D.C: The Office, 1989.
Find full textKeith, Mason. Europe's low cost airlines: An analysis of the economics and operating characteristics of Europe's charter and low cost scheduled carriers. [Cranfield]: College of Aeronautics, Cranfield University, 2000.
Find full textBook chapters on the topic "Airline Schedule"
Barnhart, Cynthia, Fang Lu, and Rajesh Shenoi. "Integrated Airline Schedule Planning." In Operations Research in the Airline Industry, 384–403. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5501-8_13.
Full textJacobs, Timothy L., Laurie A. Garrow, Manoj Lohatepanont, Frank S. Koppelman, Gregory M. Coldren, and Hadi Purnomo. "Airline Planning and Schedule Development." In International Series in Operations Research & Management Science, 35–99. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1608-1_2.
Full textLuo, Songjun, and Gang Yu. "Airline Schedule Perturbation Problem: Ground Delay Program with Splitable Resources." In Operations Research in the Airline Industry, 433–60. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5501-8_15.
Full textKim, Byung Tech, and Young Hoon Lee. "Heuristic Approach to Schedule Crew for a Regional Airline." In Computer and Information Sciences – ISCIS 2006, 65–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11902140_9.
Full textLuo, Songjun, and Gang Yu. "Airline Schedule Perturbation Problem: Landing and Takeoff with Nonsplitable Resource for the Ground Delay Program." In Operations Research in the Airline Industry, 404–32. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5501-8_14.
Full textGuo, Yufeng. "A Decision Support Framework for the Airline Crew Schedule Disruption Management with Strategy Mapping." In Operations Research Proceedings 2004, 158–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-27679-3_20.
Full textDück, Viktor, Natalia Kliewer, and Leena Suhl. "Stability of Airline Schedules." In Operations Research Proceedings 2008, 265–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00142-0_43.
Full textCamilleri, Mark Anthony. "Airline Schedules Planning and Route Development." In Tourism, Hospitality & Event Management, 179–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49849-2_11.
Full text"Airline Schedule Recovery." In Modeling Applications in the Airline Industry, 265–86. Routledge, 2016. http://dx.doi.org/10.4324/9781315595818-30.
Full text"Schedule Robustness." In Airline Network Planning and Scheduling, 345–58. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119275909.ch21.
Full textConference papers on the topic "Airline Schedule"
Haeme, R. A., J. L. Huttinger, and R. W. Shore. "Airline performance modelling to support schedule development." In the 20th conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/318123.318327.
Full textFeldman, Gregory, Paul Williams, Roger Beatty, and Richard Zelenka. "Improving airline schedule management through accurate flight arrival prediction." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-4113.
Full textLapp, Marcial, Shervin AhmadBeygi, Amy Cohn, and Omer Tsimhoni. "A recursion-based approach to simulating airline schedule robustness." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736382.
Full textNg, Kam K. H., K. L. Keung, C. K. M. Lee, and Y. T. Chow. "A Large Neighbourhood Search Approach to Airline Schedule Disruption Recovery Problem." In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2020. http://dx.doi.org/10.1109/ieem45057.2020.9309768.
Full textBerge, Matthew, Michael Carter, Aslaug Haraldsdottir, Bruno Repetto, and Laura Kang. "Airline Schedule Recovery in Flow Management: An Application for Departure Re-Routing." In 2006 ieee/aiaa 25TH Digital Avionics Systems Conference. IEEE, 2006. http://dx.doi.org/10.1109/dasc.2006.313784.
Full textHasachoo, Narat, and Ruedee Masuchun. "Factors affecting schedule nervousness in the production operations of airline catering industry." In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2015. http://dx.doi.org/10.1109/ieem.2015.7385697.
Full textTung-Kuan Liu, Yu-Ting Liu, Chiu-Hung Chen, Jyh-Horng Chou, Jinn-Tsong Tsai, and Wen-Hsien Ho. "Multi-objective optimization on robust airline schedule recover problem by using evolutionary computation." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4413946.
Full textBeatty, Roger. "Replanning the plan, or how to fix a broken airline schedule and still keep a sense of humor." In Aircraft Engineering, Technology, and Operations Congress. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-3896.
Full textHasachoo, N., and R. Masuchun. "Reducing schedule nervousness in production and operations under non-stationary stochastic demand: The case of an airline catering company." In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2016. http://dx.doi.org/10.1109/ieem.2016.7798016.
Full textDollyhigh, Samuel, Jeremy Smith, Jeffrey Viken, Antonio Trani, Hojong Baik, Nicolas Hinze, and Senanu Ahiabor. "Projecting Future Scheduled Airline Demand, Schedules, and NGATS Benefits Using TSAM." In 6th AIAA Aviation Technology, Integration and Operations Conference (ATIO). Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7749.
Full textReports on the topic "Airline Schedule"
Rupp, Nicholas, George Holmes, and Jeff DeSimone. Airline Schedule Recovery after Airport Closures: Empirical Evidence Since September 11th. Cambridge, MA: National Bureau of Economic Research, June 2003. http://dx.doi.org/10.3386/w9744.
Full text