Modeling Crew Itineraries and Delays in the National Air Transportation System

References

• AhmadBeygi B, Cohn A, Guan Y, Belobaba P (2008) Analysis of the potential for delay propagation in passenger airline networks. J. Air Transport Management 14(5):221–236.Crossref, Google Scholar
• Ahuja RK, Orlin JB (2001) Inverse optimization. Oper. Res. 49(5):771–783.Link, Google Scholar
• Allan S, Beesley A, Evans E, Gaddy S (2001) Analysis of delay causality at Newark International Airport. 4th USA/Europe Air Traffic Management R&D Seminar, Santa Fe, NM.Google Scholar
• Ball M, Barnhart C, Dresner M, Hansen M, Neels K, Odoni A, Peterson E, Sherry L, Trani A, Zou B (2010) Total delay impact study. Technical report, National Center of Excellence for Aviations Operations Research (NEXTOR), College Park, MD.Google Scholar
• Barnhart C, Vaze V (2015a) Irregular operations: Schedule recovery and robustness. Belobaba P, Odoni A, Barnhart C, eds. The Global Airline Industry, 2nd ed. (John Wiley & Sons, West Sussex, UK), 263–287.Google Scholar
• Barnhart C, Vaze V (2015b) Airline schedule optimization. Belobaba P, Odoni A, Barnhart C, eds. The Global Airline Industry, 2nd ed. (John Wiley & Sons, West Sussex, UK), 189–222.Google Scholar
• Barnhart C, Fearing D, Vaze V (2014) Modeling passenger travel and delays in the national air transportation system. Oper. Res. 62(3):580–601.Link, Google Scholar
• Barnhart C, Johnson E, Nemhauser G, Savelsbergh M, Vance P (1998) Branch-and-price: Column generation for solving huge integer programs. Oper. Res. 46(3):316–329.Link, Google Scholar
• Beatty R, Hsu R, Berry L, Rome J (1998) Preliminary evaluation of flight delay propagation through an airline schedule. 2nd USA/Europe Air Traffic Management R&D Seminar, Orlando, FL.Google Scholar
• Bureau of Transportation Statistics (2016) TranStats. On-time performance. Accessed May 24, 2018, http://www.transtats.bts.gov/Fields.asp?Table_ID=236.Google Scholar
• Cacchiani V, Salazar-González J-J (2016) Optimal solutions to a real-world integrated airline scheduling problem. Transportation Sci. 51(1):250–268.Link, Google Scholar
• Churchill A, Lovell D, Ball M (2007) Examining the temporal evolution of propagated delays at individual airports: Case studies. 7th USA/Europe ATM R&D Seminar, Barcelona, Spain.Google Scholar
• Ciruelos C, Arranz A, Etxebarria I, Peces S, Campanelli B, Fleurquin P, Eguiluz V, Ramasco J (2015) Modelling delay propagation trees for scheduled flights. 11th USA/Europe Air Traffic Management R&D Seminar, Lisbon, Portugal.Google Scholar
• Cook A, Tanner G (2015) Delay propagation—New metrics, new insights. 11th USA/Europe Air Traffic Management R&D Seminar, Lisbon, Portugal.Google Scholar
• Desaulniers G, Desrosiers J, Solomon M (2005) Column Generation (Springer, New York).Crossref, Google Scholar
• Duan Z, Wang L (2011) Heuristic algorithms for the inverse mixed integer linear programming problem. J. Global Optim. 51(3):463–471.Crossref, Google Scholar
• Dunbar M, Froyland G, Wu C (2012) Robust airline schedule planning: Minimizing propagated delay in an integrated routing and crewing framework. Transportation Sci. 46(2):204–216.Link, Google Scholar
• Ehrgott M, Ryan DM (2002) Constructing robust crew schedules with bicriteria optimization. J. Multi-Criteria Decision Anal. 11(3):139–150.Crossref, Google Scholar
• Fleurquin P, Ramasco J, Eguiluz V (2013) Data-driven modeling of systemic delay propagation under severe meteorological conditions. 10th USA/Europe Air Traffic Management R&D Seminar, Chicago.Google Scholar
• Gao C, Johnson E, Smith B (2009) Integrated airline fleet and crew robust planning. Transportation Sci. 43(1):2–16.Link, Google Scholar
• Irnich S, Desaulniers G (2005) Shortest path problems with resource constraints. Desaulniers G, Desrosiers J, Solomon MM, eds. Column Generation (Springer, New York), 33–65.Crossref, Google Scholar
• Jacquillat A, Odoni A (2015) An integrated scheduling and operations approach to airport congestion mitigation. Oper. Res. 63(6):1390–1410.Link, Google Scholar
• Kasirzadeh A, Saddoune M, Soumis F (2015) Airline crew scheduling: Models, algorithms, and data sets. Eur. J. Transportation Logist. 6:1–27.Google Scholar
• Lamperski J, Schaefer A (2015) A polyhedral characterization of the inverse-feasible region of a mixed-integer program. Oper. Res. Lett. 43(6):575–578.Crossref, Google Scholar
• Lu D, Gzara F (2015) The robust crew pairing problem: Model and solution methodology. J. Global Optim. 62:29–54.Crossref, Google Scholar
• Mercier A, Cordeau JF, Soumis F (2005) A computational study of Benders decomposition for the integrated aircraft routing and crew scheduling problem. Comput. Oper. Res. 32:1451–1476.Crossref, Google Scholar
• Office of Inspector General (2010) New York flight delays have three main causes, but more work is needed to understand their nationwide effect. Technical Report AV-2011-007, Office of Inspector General, U.S. Department of Transportation, Washington, DC.Google Scholar
• Petersen JD, Sölveling G, Clarke J-P, Johnson EL, Shebalov S (2012) An optimization approach to airline integrated recovery. Transportation Sci. 46(4):482–500.Link, Google Scholar
• Pyrgiotis N, Malone KM, Odoni A (2013) Modeling delay propagation within an airport network. Transportation Res. Part C: Emerging Tech. 27:60–75.Crossref, Google Scholar
• Rosenberger J, Schaefer A, Goldsman D, Johnson E, Kleywegt A, Nemhauser G (2002) A stochastic model of airline operations. Transportation Sci. 36(4):357–377.Link, Google Scholar
• Schaefer A, Johnson E, Kleywegt A, Nemhauser G (2005) Airline crew scheduling under uncertainty. Transportation Sci. 39(3):340–348.Link, Google Scholar
• Shebalov S, Klabjan D (2006) Robust airline crew pairing: Move-up crews. Transportation Sci. 40(3):300–312.Link, Google Scholar
• SimAir User’s Manual (2003) SIMAIR user’s manual. Accessed May 24, 2018, https://www.ise.nus.edu.sg/project/simair/download/manual.html.Google Scholar
• Smith B, Johnson E (2006) Robust airline fleet assignment: Imposing station purity using station decomposition. Transportation Sci. 40(4):497–516.Link, Google Scholar
• Swaroop P, Zou B, Ball MO, Hansen M (2012) Do more U.S. airports need slot controls? A welfare based approach to determine slot levels. Transportation Res. Part B: Methodological 46(9):1239–1259.Crossref, Google Scholar
• Tam B, Ehrgott M, Ryan D, Zakeri G (2011) A comparison of stochastic programming and bi-objective optimization approaches to robust airline crew scheduling. OR Spectrum 33(1):49–75.Crossref, Google Scholar
• U.S. Government Accountability Office (2008) Commercial aviation: Impact of airline crew scheduling on delays and cancellations of commercial flights. Report GAO-08-1041R, U.S. Government Accountability Office, Washington, DC.Google Scholar
• Vance P, Atamturk A, Barnhart C, Gelman E, Johnson E, Krishna A, Mahidhara D, Nemhauser G (1997) A heuristic branch-and-price approach for the airline crew pairing problem. Technical Report LEC-97-06, Georgia Institute of Technology, Atlanta.Google Scholar
• Vaze V, Barnhart C (2012) Modeling airline frequency competition for airport congestion mitigation. Transportation Sci. 46(4):512–535.Link, Google Scholar
• Wang L (2013) Branch-and-bound algorithms for the partial inverse mixed integer linear programming problem. J. Global Optim. 55(3):491–506.Crossref, Google Scholar
• Weide O, Ryan D, Ehrgott M (2010) An iterative approach to robust and integrated aircraft routing and crew scheduling. Comput. Oper. Res. 37(5):833–844.Crossref, Google Scholar
• Wong J, Tsai S (2012) A survival model for flight delay propagation. J. Air Transport Management 23:5–11.Crossref, Google Scholar
• Xu N, Donohue G, Laskey K, Chen CH (2005) Estimation of delay propagation in the national aviation system using Bayesian networks. 6th USA/Europe Air Traffic Management R&D Seminar, Baltimore.Google Scholar
• Yen JW, Birge JR (2006) A stochastic programming approach to the airline crew scheduling problem. Transportation Sci. 40(1):3–14.Link, Google Scholar
• Zografos K, Salouras Y, Madas M (2012) Dealing with the efficient allocation of scarce resources at congested airports. Transportation Res. Part C: Emerging Tech. 21:244–256.Crossref, Google Scholar