Dynamic airline scheduling and robust airline schedule de-peaking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes 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

OPTIMIZATION APPROACHES TO AIRLINE INDUSTRY CHALLENGES: Airline Schedule Planning and Recovery

The airline industry has a long history of developing and applying optimization approaches to their myriad of scheduling problems, including designing flight schedules that maximize profitability while satisfying rules related to aircraft maintenance; generating cost-minimizing, feasible work schedules for pilots and flight attendants; and identifying implementable, low-cost changes to aircraft and crew schedules as disruptions render the planned schedule inoperable. The complexities associated with these problems are immense, including long-and short-term planning horizons; and multiple resources including aircraft, crews, and passengers, all operating over shared airspace and airport capacity. Optimization approaches have played an important role in overcoming this complexity and providing effective aircraft and crew schedules. Historical optimization-based approaches, however, often involve a sequential process, first generating aircraft schedules and then generating crew schedules. Decisions taken in the first steps of the process limit those that are possible in subsequent steps, resulting in overall plans that, while feasible, are typically sub-optimal. To mitigate the myopic effects of sequential solutions, researchers have developed extended models that begin to integrate som

Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009), it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones

A decomposition approach for commodity pickup and delivery with time-windows under uncertainty

We consider a special class of large-scale, network-based, resource allocation problems under uncertainty, namely that of multi-commodity flows with time-windows under uncertainty. In this class, we focus on problems involving commodity pickup and delivery with time-windows. Our work examines methods of proactive planning, that is, robust plan generation to protect against future uncertainty. By a priori modeling uncertainties in data corresponding to service times, resource availability, supplies and demands, we generate solutions that are more robust operationally, that is, more likely to be executed or easier to repair when disrupted. We propose a novel modeling and solution framework involving a decomposition scheme that separates problems into a routing master problem and Scheduling Sub-Problems; and iterates to find the optimal solution. Uncertainty is captured in part by the master problem and in part by the Scheduling Sub-Problem. We present proof-of-concept for our approach using real data involving routing and scheduling for a large shipment carrier’s ground network, and demonstrate the improved robustness of solutions from our approach

Un modelo integrado para el enrutamiento de aeronaves y la programación de la tripulación: Relajación lagrangiana y algoritmo metaheurístico

[EN] Airline optimization is a significant problem in recent researches and airline industryl as it can determine the level of service, profit and competition status of the airline. Aircraft and crew are expensive resources that need efficient utilization. This paper focuses simultaneously on two major issues including aircraft maintenance routing and crew scheduling. Several key issues such as aircraft replacement, fairly night flights assignment and long-life aircrafts are considered in this model. We used the flight hours as a new framework to control aircraft maintenance. At first, an integrated mathematical model for aircraft routing and crew scheduling problems is developed with the aim of cost minimization. Then, Lagrangian relaxation and Particle Swarm Optimization algorithm (PSO) are used as the solution techniques. To evaluate the efficiency of solution approaches, model is solved with different numerical examples in small, medium and large sizes and compared with GAMS output. The results show that Lagrangian relaxation method provides better solutions comparing to PSO and also has a very small gap to optimum solution.[ES] La optimización de aerolíneas es un problema importante en investigaciones recientes e industria de aerolíneas, ya que puede determinar el nivel de servicio, el beneficio y el estado de competencia de la aerolínea. Las aeronaves y la tripulación son recursos costosos que necesitan una utilización eficiente. Este artículo se centra simultáneamente en dos cuestiones principales, incluyendo el enrutamiento de mantenimiento de aeronaves y la programación de la tripulación. En este modelo se consideran varios temas clave, como el reemplazo de aeronaves, la asignación de vuelos nocturnos y los aviones envejecidos. Usamos las horas de vuelo como un nuevo marco para controlar el mantenimiento de las aeronaves. Al principio, se desarrolla un modelo matemático integrado para el enrutamiento de aeronaves y los problemas de programación de la tripulación con el objetivo de la minimización de costos. A continuación, se utilizan como técnicas de solución la relajación lagran-giana y el algoritmo “Particle Swarm Optimization” (PSO). Para evaluar la eficiencia de los en-foques de la solución, el modelo se resuelve con diferentes ejemplos numéricos en tamaños pequeños, medianos y grandes y se compara con la salida GAMS. Los resultados muestran que el método de relajación lagrangiana proporciona mejores soluciones en comparación con PSO y también tiene una pequeña diferencia para una solución óptimaMirjafari, M.; Rashidi Komijan, A.; Shoja, A. (2020). An integrated model for aircraft routing and crew scheduling: Lagrangian Relaxation and metaheuristic algorithm. WPOM-Working Papers on Operations Management. 11(1):25-38. https://doi.org/10.4995/wpom.v11i1.12891OJS2538111Al-Thani, Nayla Ahmad, Ben Ahmed, Mohamed and Haouari, Mohamed (2016). A model and optimization-based heuristic for the operational aircraft maintenance routing problem, Transportation Research Part C: Emerging Technologies, Volume 72, Pages 29-44. https://doi.org/10.1016/j.trc.2016.09.004Azadeh, A., HosseinabadiFarahani, M., Eivazy, H., Nazari-Shirkouhi, S., &Asadipour, G. (2013). A hybrid meta-heuristic algorithm for optimization of crew scheduling, Applied Soft Computing, Volume 13, Pages 158-164. https://doi.org/10.1016/j.asoc.2012.08.012Barnhart C. and Cohn, A. (2004). Airline schedule planning: Accomplishments and opportunities, Manufacturing & Service Operations Management, 6(1):3-22, 47, 69, 141, 144. https://doi.org/10.1287/msom.1030.0018Basdere, Mehmet and Bilge, Umit (2014). Operational aircraft maintenance routing problem with remaining time consideration, European Journal of Operational Research, Volume 235, Pages 315-328. https://doi.org/10.1016/j.ejor.2013.10.066Bazargan, Massoud (2010). Airline Operations and scheduling second edition, Embry-Riddle Aeronautical University, USA, Ashgate publishing limite.Belien, Jeroen, Demeulemeester, Eric and Brecht (2010). Integrated staffing and scheduling for an aircraft line maintenance problem, HUB RESEARCH PAPER Economics & Management.Ben Ahmed, M., Zeghal Mansour, Farah and Haouari, Mohamed (2018). Robust integrated maintenance aircraft routing and crew pairing, Journal of Air Transport Management, Volume 73, Pages 15-31. https://doi.org/10.1016/j.jairtraman.2018.07.007Ben Ahmed, M., Zeghal Mansour, F., Haouari, M. (2017). A two-level optimization approach for robust aircraft routing and retiming, Computers and Industrial Engineering, Volume 112, Pages 586-594. https://doi.org/10.1016/j.cie.2016.09.021Borndorfer, R., Schelten, U., Schlechte, T., Weider, S. (2006). A column generation approach to airline crew scheduling, Springer Berlin Heidelberg, Pages 343-348. https://doi.org/10.1007/3-540-32539-5_54Clarke, L., E. Johnson, G. Nemhauser, Z. Zhu. (1997). The Aircraft Rotation Problem. Annals of Operations Research, 69, Pages 33-46. https://doi.org/10.1023/A:1018945415148Deveci, Muhammet and ÇetinDemirel, Nihan (2018). Evolutionary algorithms for solving the airline crew pairing problem, Computers & Industrial Engineering, Volume 115, Pages 389-406. https://doi.org/10.1016/j.cie.2017.11.022Dozic, S., Kalic, M. (2015). Three-stage airline fleet planning model, J. Air Transport. Manag, 43, Pages 30-39. https://doi.org/10.1016/j.jairtraman.2015.03.011Eltoukhy, A.E., Chan, F.T., Chung, S. (2017). Airline schedule planning: a review and future directions, Ind. Manag. Data Syst, 117(6), Pages 1201-1243. https://doi.org/10.1108/IMDS-09-2016-0358Feo, T. A., J. F. Bard. (1989). Flight Scheduling and Maintenance Base Planning. Management Science, 35(12), Pages 1415-1432. https://doi.org/10.1287/mnsc.35.12.1415Goffin, J. L. (1977). On the convergence rates of subgradient optimization methods. Math. Programming, 13, Pages 329-347. https://doi.org/10.1007/BF01584346Gopalakrishnan, B., Johnson, E. L (2005). Airline crew scheduling, State-of-the-art. Ann. Oper. Res, 140(1), Pages 305-337. https://doi.org/10.1007/s10479-005-3975-3Held, M. and Karp, R.M. (1970). The Traveling-Salesman Problem and Minimum SpanningTrees. Operations Research, 18, 1138-1162. https://doi.org/10.1287/opre.18.6.1138Held, M. Wolfe, P., Crowder, H. D. (1974). Validation of subgradient optimization, Math. Programming, 6, 62-88. https://doi.org/10.1007/BF01580223Jamili, Amin (2017). A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling, with consideration of fleet assignment problem, Journal of Air Transport Management, Volume 58, Pages 21-30. https://doi.org/10.1016/j.jairtraman.2016.08.008Jiang, H., Barnhart, C. (2009) Dynamic airline scheduling, Transport. Sci, 43(3), Pages 336-354. https://doi.org/10.1287/trsc.1090.0269Kasirzadeh, A., Saddoune, M., Soumis, F. (2015). Airline crew scheduling: models, algorhitms and data sets, Euro Journal on Transportation and Logistics, 6(2), Pages 111-137. https://doi.org/10.1007/s13676-015-0080-xLacasse-Guay, E., Desaulniers, G., Soumis, F. (2010). Aircraft routing under different business processes, J. Air Transport. Manag, 16(5), Pages 258-263. https://doi.org/10.1016/j.jairtraman.2010.02.001Muter, İbrahim, Birbil, Ş. İlker, Bülbül, Kerem, Şahin, Güvenç,Yenigün, Hüsnü, Taş,Duygu andTüzün, Dilek (2013). Solving a robust airline crew pairing problem with column generation, Computers & Operations Research, Volume 40, Issue 3, Pages 815-830. https://doi.org/10.1016/j.cor.2010.11.005Saddoune, Mohammed, Desaulniers, Guy, Elhallaoui, Issmail and François Soumis (2011). Integrated airline crew scheduling: A bi-dynamic constraint aggregation method using neighborhoods, European Journal of Operational Research, Volume 212, Pages 445-454. https://doi.org/10.1016/j.ejor.2011.02.009Safaei, Nima and K.S.Jardine, Andrew (2018). Aircraft routing with generalized maintenance constraints, Omega, Volume 80, Pages 111-122. https://doi.org/10.1016/j.omega.2017.08.013Shao Shengzhi (2012). Integrated Aircraft Fleeting, Routing, and Crew Pairing Models and Algorithms for the Airline Industry, Faculty of the Virginia Polytechnic Institute and State University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Industrial and Systems Engineering.Shao, S., Sherali, H.D., Haouari, M. (2017). A novel model and decomposition approach for the integrated airline fleet assignment, aircraft routing, crew pairing problem, Transport. Sci, 51(1), Pages 233-249. https://doi.org/10.1287/trsc.2015.0623Sherali, H.D., Bish, E.K., Zhu, X. (2006). Airline fleet assignment concepts, models and algorithms, Eur. J. Oper. Res, 172(1), Pages 1-30. https://doi.org/10.1016/j.ejor.2005.01.056Warburg, V., Hansen, T.G., Larsen, A., Norman, H., Andersson, E. (2008). Dynamic airline scheduling: an analysis of potentials of refleeting and retiming, J. Air Transport. Manag. 14(4), Pages 163-167. https://doi.org/10.1016/j.jairtraman.2008.03.004Yan, C. and Kung, J. (2018). Robust aircraft routing, Transport. Sci, 52(1), Pages 118-133. https://doi.org/10.1287/trsc.2015.0657Yen, J.W., Birge, J.R., (2006). A stochastic programming approach to the airline crew scheduling problem. Transportation Science, Volume 40, Pages 3-14. https://doi.org/10.1287/trsc.1050.0138Yu, G. (1998). Operation Research in the Airline Industry. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-5501-8Zeren, Bahadir and Ozkol, Ibrahim (2016). A novel column generation strategy foe large scale airline crew pairing problems, Expert system with applications, Volume 55, Pages 133-144. https://doi.org/10.1016/j.eswa.2016.01.045Zhang, Dong, Lau, H.Y.K. Henry and Yu, Chuhang (2015). A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems, Computers and Industrial Engineering, Volume 87, Pages 436-453. https://doi.org/10.1016/j.cie.2015.05.03

Reallocation Problems in Scheduling

In traditional on-line problems, such as scheduling, requests arrive over time, demanding available resources. As each request arrives, some resources may have to be irrevocably committed to servicing that request. In many situations, however, it may be possible or even necessary to reallocate previously allocated resources in order to satisfy a new request. This reallocation has a cost. This paper shows how to service the requests while minimizing the reallocation cost. We focus on the classic problem of scheduling jobs on a multiprocessor system. Each unit-size job has a time window in which it can be executed. Jobs are dynamically added and removed from the system. We provide an algorithm that maintains a valid schedule, as long as a sufficiently feasible schedule exists. The algorithm reschedules only a total number of O(min{log^* n, log^* Delta}) jobs for each job that is inserted or deleted from the system, where n is the number of active jobs and Delta is the size of the largest window.Comment: 9 oages, 1 table; extended abstract version to appear in SPAA 201

Long Term Traffic Forecasts and Operating Pattern for a Regional Airport

A common problem facing airport planners is to estimate the detail of the flight schedule that will be operated at an airport in future years. This is important to assess issues such as capacity requirements, noise and emission outputs and connectivity. For airline commercial analysts it is necessary to understand the competitive pressures and opportunities that will develop in different markets. This paper uses an approach adopted for the VANTAGE project, conducted for the UK Department of Trade and Industry to forecast the detail of airline operations at UK regional airports on a 10-20 year horizon. The paper considers a generalised forecast at the route level and how this can then be disaggregated into detail such as aircraft size, airline and flight schedule. The existing routes and frequencies are used as a basis. The detailed history of service to each international destination from the UK is used to model the future service. The extra frequencies created by the traffic model are then allocated to airlines and time windows using the D’Hondt method of highest averages. An example peak week schedule is presented for Aberdeen airport accompanied by a commentary on the key strategic and policy implications for the industry

Integrated robust airline schedule development

In air transportation, airline profitability is influenced by the airline's ability to build flight schedules. In order to generate operational schedules, airlines engage in a complex decision-making process, referred to as airline schedule planning. Up to now, the generation of flight schedules has been separated and optimized sequentially. The schedule design has been traditionally decomposed into two sequential steps. The frequency planning and the timetable development. The purpose of the second problem of schedule development, fleet assignment, is to assign available aircraft types to flight legs such that seating capacity on an assigned aircraft matches closely with flight demand and such that costs are minimized. Our work integrates these planning phases into one single model in order to produce more economical solutions and create fewer incompatibilities between the decisions. We propose an integrated robust approach for the schedule development step. We design the timetable ensuring that enough time is available to perform passengers’ flight connections, making the system robust avoiding misconnected passengers. An application of the model for a simplified IBERIA network is shown