Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

Abstract The airline schedule planning problem is defined as the sequence of decisions that need to be made to obtain a fully operational flight schedule. Historically, the airline scheduling problem has been sequentially solved. However, there have already been many attempts in order to obtain airline schedules in an integrated way. But due to tractability issues it is nowadays impossible to determine a fully operative and optimal schedule with an integrated model which accounts for all the key airline related aspects such as competitive effects, stochastic demand figures and uncertain operating conditions. Airlines usually develop base schedules, which are obtained much time in advance to the day of operations and not accounting for all the related uncertainty. This paper proposes a mathematical model in order to update base schedules in terms of timetable and fleet assignments while considering stochastic demand figures and uncertain operating conditions, and where robust itineraries are introduced in order to ameliorate miss-connected passengers. The proposed model leads to a large-scale problem which is difficult to be solved. Therefore, a novel improved and accelerated Benders decomposition approach is proposed. The analytical work is supported with case studies involving the Spanish legacy airline, IBERIA. The presented approach shows that the number of miss-connected passengers may be reduced when robust planning is applied.

[1]  Gary Froyland,et al.  Robust Airline Schedule Planning: Minimizing Propagated Delay in an Integrated Routing and Crewing Framework , 2012, Transp. Sci..

[2]  George L. Nemhauser,et al.  The fleet assignment problem: Solving a large-scale integer program , 1995, Math. Program..

[3]  Cynthia Barnhart,et al.  Airline Schedule Planning: Integrated Models and Algorithms for Schedule Design and Fleet Assignment , 2004, Transp. Sci..

[4]  Luis Cadarso,et al.  Robust passenger oriented timetable and fleet assignment integration in airline planning , 2013 .

[5]  R. Ratliff,et al.  A multi-flight recapture heuristic for estimating unconstrained demand from airline bookings , 2008 .

[6]  Cynthia Barnhart,et al.  The Global Airline Industry , 2009 .

[7]  Eduardo Saliby,et al.  Descriptive Sampling: A Better Approach to Monte Carlo Simulation , 1990 .

[8]  Cynthia Barnhart,et al.  Robust flight schedules through slack re-allocation , 2013, EURO J. Transp. Logist..

[9]  Henry Y. K. Lau,et al.  A math-heuristic algorithm for the integrated air service recovery , 2016 .

[10]  Sinan Gürel,et al.  Integrated aircraft and passenger recovery with cruise time controllability , 2016, Ann. Oper. Res..

[11]  Mohamed Haouari,et al.  A two-level optimization approach for robust aircraft routing and retiming , 2017, Comput. Ind. Eng..

[12]  Amy Cohn,et al.  Decreasing airline delay propagation by re-allocating scheduled slack , 2010 .

[13]  Juan José Salazar González,et al.  Optimal Solutions to a Real-World Integrated Airline Scheduling Problem , 2017, Transp. Sci..

[14]  J. F. Benders Partitioning procedures for solving mixed-variables programming problems , 1962 .

[15]  Shangyao Yan,et al.  An airline scheduling model and solution algorithms under stochastic demands , 2008, Eur. J. Oper. Res..

[16]  Cynthia Barnhart,et al.  Modeling Airline Frequency Competition for Airport Congestion Mitigation , 2012, Transp. Sci..

[17]  Hai Jiang,et al.  Robust airline schedule design in a dynamic scheduling environment , 2011, Comput. Oper. Res..

[18]  Gilbert Laporte,et al.  A column generation post-optimization heuristic for the integrated aircraft and passenger recovery problem , 2016, Comput. Oper. Res..

[19]  Diego Klabjan,et al.  Airline Crew Scheduling , 2003 .

[20]  Alexander Shapiro,et al.  On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs , 2000, SIAM J. Optim..

[21]  Hanif D. Sherali,et al.  Airline fleet assignment concepts, models, and algorithms , 2006, Eur. J. Oper. Res..

[22]  Alexander Shapiro,et al.  The Sample Average Approximation Method for Stochastic Discrete Optimization , 2002, SIAM J. Optim..

[23]  E. Silver,et al.  Some insights regarding selecting sets of scenarios in combinatorial stochastic problems , 1996 .

[24]  Nikolaos Papadakos,et al.  Integrated airline scheduling , 2009, Comput. Oper. Res..

[25]  António Pais Antunes,et al.  Integrated Flight Scheduling and Fleet Assignment Under Airport Congestion , 2013, Transp. Sci..

[26]  Ángel Marín,et al.  Integrated Robust Airline Schedule Development , 2011 .

[27]  Matthew E. Berge,et al.  Demand Driven Dispatch: A Method for Dynamic Aircraft Capacity Assignment, Models and Algorithms , 1993, Oper. Res..

[28]  Cynthia Barnhart,et al.  Airline Schedule Planning: Accomplishments and Opportunities , 2004, Manuf. Serv. Oper. Manag..

[29]  Thomas L. Magnanti,et al.  Accelerating Benders Decomposition: Algorithmic Enhancement and Model Selection Criteria , 1981, Oper. Res..

[30]  François Soumis,et al.  Passenger Flow Model for Airline Networks , 2007, Transp. Sci..

[31]  Jean-François Cordeau,et al.  A computational study of Benders decomposition for the integrated aircraft routing and crew scheduling problem , 2003, Comput. Oper. Res..

[32]  Sinan Gürel,et al.  An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control , 2016 .

[33]  Ger Koole,et al.  Estimating unconstrained demand rate functions using customer choice sets , 2011 .

[34]  Alexander Shapiro,et al.  The empirical behavior of sampling methods for stochastic programming , 2006, Ann. Oper. Res..

[35]  Chiwei Yan,et al.  Robust Aircraft Routing , 2016 .

[36]  Mohamed Haouari,et al.  A model for enhancing robustness of aircraft and passenger connections , 2013 .

[37]  Cynthia Barnhart,et al.  Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail , 2017, Transp. Sci..

[38]  Larry Weatherford,et al.  Better unconstraining of airline demand data in revenue management systems for improved forecast accuracy and greater revenues , 2002 .

[39]  François Soumis,et al.  Improving the Objective Function of the Fleet Assignment Problem , 2007 .

[40]  Cynthia Barnhart,et al.  Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions , 2006, Transp. Sci..

[41]  Michel Bierlaire,et al.  An Integrated Airline Scheduling, Fleeting, and Pricing Model for a Monopolized Market , 2014, Comput. Aided Civ. Infrastructure Eng..

[42]  Rommert Dekker,et al.  A Scenario Aggregation - Based Approach for Determining a Robust Airline Fleet Composition for Dynamic Capacity Allocation , 2002, Transp. Sci..

[43]  W. Swan Airline demand distributions: passenger revenue management and spill , 2002 .

[44]  Jacques Desrosiers,et al.  Daily Aircraft Routing and Scheduling , 1994 .