High level rule modeling language for airline crew pairing

The crew pairing problem is an airline optimization problem where a set of least costly pairings (consecutive flights to be flown by a single crew) that covers every flight in a given flight network is sought. A pairing is defined by using a very complex set of feasibility rules imposed by international and national regulatory agencies, and also by the airline itself. The cost of a pairing is also defined by using complicated rules. When an optimization engine generates a sequence of flights from a given flight network, it has to check all these feasibility rules to ensure whether the sequence forms a valid pairing. Likewise, the engine needs to calculate the cost of the pairing by using certain rules. However, the rules used for checking the feasibility and calculating the costs are usually not static. Furthermore, the airline companies carry out what-if-type analyses through testing several alternate scenarios in each planning period. Therefore, embedding the implementation of feasibility checking and cost calculation rules into the source code of the optimization engine is not a practical approach. In this work, a high level language called ARUS is introduced for describing the feasibility and cost calculation rules. A compiler for ARUS is also implemented in this work to generate a dynamic link library to be used by crew pairing optimization engines

Robust crew pairing for managing extra flights /

The airline industry encounters many optimization problems such as scheduling flights, assigning the fleet, scheduling the crew. Among them, the crew scheduling problem is the most studied one. The main reason is that the crew cost is one of the largest components of the operational cost for an airline company. Therefore, there are many models proposed in the literature to find a cost efficient crew schedule. Most of those models divide the crew scheduling problem into two separate problems, namely the crew pairing and the crew assignment problems. The crew pairing problem that we study here aims at finding the least costly subset of pairings, which cover the scheduled flights. Although there are many approaches to solve the crew pairing problem, most of them assume no disruptions during the operation. However disruptions due to weather conditions, maintenance problems, and so on are common problems leading to higher operational crew cost in practice. These kinds of disruptions result in delaying or canceling some scheduled flights. Another disruption that local airline companies face is adding an extra flight to predetermined (regular) flight schedule. In this study, we propose a model that provides robust crew pairing schedule in the case of adding an extra flight to the regular flight schedule. Two solution approaches are along with the mathematical model are proposed. The objective of the proposed model is to maximize the total number of solutions, while maintaining the increase in the crew cost at an acceptable level. A crew pairing problem is then solved by both the proposed model and the conventional model. Finally, computational experiments are conducted to demonstrate the benefits of the proposed model

High level rule modelling language for airline crew pairing: design and implementation

The crew pairing problem is an airline optimization problem where a set of least costly pairings (consecutive flights to be flown by a single crew) that covers every flight in a given flight network is sought. A pairing is defined by using a complex set of feasibility rules imposed by international and national regulatory agencies, and also by the airline itself. The cost of a pairing is also defined using some complicated rules. When an optimization engine generates a sequence of flights from a given flight network, it has to check all these feasibility rules to understand if the sequence is a valid pairing, and has to calculate the cost of the pairing by using the cost calculation rules. However the feasibility and cost calculation rules are not usually stable. Airline companies try several scenarios in each planning period. In this work, a high level language for describing the feasibility and cost calculation rules is designed. Airline companies can use such a domain specific language to specify the rules for feasibility and cost calculation. A compiler for this language is also implemented which generates a dynamic library implementing the specified rules

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. 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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). 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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). 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Solving a robust airline crew pairing problem with column generation

In this study, we solve a robust version of the airline crew pairing problem. Our concept of robustness was partially shaped during our discussions with small local airlines in Turkey which may have to add a set of extra flights into their schedule at short notice during operation. Thus, robustness in this case is related to the ability of accommodating these extra flights at the time of operation by disrupting the original plans as minimally as possible. We focus on the crew pairing aspect of robustness and prescribe that the planned crew pairings incorporate a number of predefined recovery solutions for each potential extra flight. These solutions are implemented only if necessary for recovery purposes and involve either inserting an extra flight into an existing pairing or partially swapping the flights in two existing pairings in order to cover an extra flight. The resulting mathematical programming model follows the conventional set covering formulation of the airline crew pairing problem typically solved by column generation with an additional complication. The model includes constraints that depend on the columns due to the robustness consideration and grows not only column-wise but also row-wise as new columns are generated. To solve this dicult model, we propose a row and column generation approach. This approach requires a set of modifications to the multi-label shortest path problem for pricing out new columns (pairings) and various mechanisms to handle the simultaneous increase in the number of rows and columns in the restricted master problem during column generation. We conduct computational experiments on a set of real instances compiled from a local airline in Turkey

Airline crew scheduling

An airline must cover each flight leg with a full complement of cabin crew in a manner consistent with safety regulations and award requirements. Methods are investigated for solving the set partitioning and covering problem. A test example illustrates the problem and the use of heuristics. The Study Group achieved an understanding of the problem and a plan for further work

Disruption management in passenger railway transportation.

This paper deals with disruption management in passengerrailway transportation. In the disruption management process, manyactors belonging to different organizations play a role. In this paperwe therefore describe the process itself and the roles of thedifferent actors.Furthermore, we discuss the three main subproblems in railwaydisruption management: timetable adjustment, and rolling stock andcrew re-scheduling. Next to a general description of these problems,we give an overview of the existing literature and we present somedetails of the specific situations at DSB S-tog and NS. These arethe railway operators in the suburban area of Copenhagen, Denmark,and on the main railway lines in the Netherlands, respectively.Since not much research has been carried out yet on OperationsResearch models for disruption management in the railway context,models and techniques that have been developed for related problemsin the airline world are discussed as well.Finally, we address the integration of the re-scheduling processesof the timetable, and the resources rolling stock and crew.