Airline Scheduling Process
2.6 Summary, Conclusion, and Future Challenges .1 Summary.1Summary
An airline schedule represents the central element within an airline’s corporate plan- ning framework and consists of two elements:
2.6 Summary, Conclusion, and Future Challenges 43
• the flight schedule that contains detailed information on the offered flights (like departure and arrival times and airports) and that is presented to potential pas- sengers, and
• the assignment of aircraft and crew resources to the flights.
The airline schedule is an airline’s primary marketing tool, having the largest in- fluence on passenger demand. Furthermore, it affects every operational decision.
Given an airline schedule, a significant portion of revenues and costs is fixed. Conse- quently, airline scheduling is one of the most important planning tasks in an airline.
However, because of the large number of factors that have to be taken into account simultaneously when constructing or optimizing an airline schedule, this problem also represents the most demanding challenge for the airline.
Because of its complexity, the consensus today is that a single model for the air- line scheduling problem is computationally intractable and that this planning prob- lem is best solved in a sequential process. The overall problem is decomposed into subproblems of less complexity that are solved in a sequence. Subproblems are then aggregated to phases in the airline scheduling process. One possible decomposi- tion and solution process can be found on page 10, consisting of the phases flight schedule generation, aircraft scheduling, and crew scheduling. The purpose of this chapter was to introduce these subproblems including their objectives and major constraints as well as presenting optimization models that were developed to solve these subproblems.
Solution approaches to more than one subproblem were presented in Sect. 2.5.
Many researchers developed solution approaches that integrate two or more sub- problems of the airline scheduling problem and reported (significant) improvements in solution quality. In some models, a second subproblem is only approximated. For example, if in a fleet assignment model maintenance considerations are included by ensuring sufficient maintenance opportunities, the aircraft routing problem might still be infeasible because the spacing of maintenance visits and individual aircraft were not considered (Clarke et al., 1996; Clarke et al., 1997). One interesting result of the previous section is that there is only little work integrating crew scheduling problems to the flight schedule generation and aircraft scheduling phase. Although there might be a high potential for increases in profits because of high crew costs, in this study it is believed that the following three facts might be the main reasons for this observation:
• Crew work-rules are highly affected by company policies and legal restrictions, thus, these rules may differ from airline to airline and country to country, as do many crew costs. Because of this, an optimization approach that solves a generic crew scheduling problem might be of theoretical interest only.
• Because of the high number and variety of restrictions, the crew scheduling problem is a very complex problem by itself, and, thus, it is even harder to solve a combination of this problem and another airline scheduling problem.
• To solve the crew scheduling problem, human factors like team quality and satisfaction have to be considered that are hard to quantify. Thus, many crew
scheduling problems are still solved manually or using simple heuristics em- bedded in systems that support key decisions of human schedulers.
2.6.2 Conclusion
Optimal solutions of the airline scheduling problem can only be realized if all rele- vant variables, their interdependencies, and restrictions are combined in one model of considerable detail, and a solution algorithm that guarantees to find the opti- mal solution is applied. Optimal solutions to subproblems do not imply an optimal overall solution. When decomposing a problem, interdependencies between sub- problems cannot be considered. Solving subproblems in a sequence will result in less freedom for later planning steps, because the solution of one planning step serves as given input for the succeeding problem. Thus, solutions are unsatisfac- tory or even infeasible (Barnhart et al., 1998; Cordeau et al., 2001; Mashford &
Marksjo, 2001; Yan & Tseng, 2002; Cohn & Barnhart, 2003; Barnhart et al., 2003;
Guo et al., 2006). To overcome these problems, airlines have to implement (costly and time consuming) feedback-loops or iterations in their airline scheduling pro- cess (Grandeau et al., 1998; Andersson et al., 1998). Information of later planning steps are propagated to previous subproblems to alter their solutions. To relax the boundaries between the individual solution steps, models have to be developed that include decision variables of more subproblems. This trend towards an integrated airline scheduling model can be recognized in recent publications.
In the beginning of modeling the airline scheduling problem, computational power was limited and research was focused on single subproblems with simpli- fying assumptions (Desaulniers et al., 1997; Sriram & Haghani, 2003). Advances in optimization theory and computer hardware then led to (Yu, 1998; Barnhart et al., 2003)
• the application of exact solution approaches and, thus, a higher solution quality (Rushmeier & Kontogiorgis, 1997),
• more realistic models, as a problem could be formulated more detailed with a higher number of practical requirements, with less simplifications, and for more realistic problem sizes,
• the extension of the scope of problems by integrating different subproblems (or elements) of the airline scheduling problem.
In general, each airline scheduling model represents a trade-off between these three directions. If more complex problems are considered either by increasing the level of detail or by extending the scope, usually heuristics or exact approaches including heuristic elements are applied. For example, many heuristics are used to solve crew scheduling problems or to enhance exact approaches in this scheduling phase. Some problems are still solved manually with little optimization because either no models exist or they contain major simplifications that lead to an unrealistic problem formulation. For example, most of the presented models for flight schedule generation do not consider relevant restrictions or costs of airline resources, and
2.6 Summary, Conclusion, and Future Challenges 45 if so, they represent this information only on a very rough and unrealistic basis.
Thus, the flight schedule generation phase is still performed manually with much subjective judgment and decision making. There is little optimization in managing the interrelation between supply and demand directly, systematically, and accurately (Yan & Tseng, 2002).
2.6.3 Future Challenges
The models presented in this study differ in the amount, variety and accuracy of simplifying assumptions. One common characteristic to almost every model is the postulation of deterministic data like fixed demand (or its distribution), flight times, and turn times. However, in reality many of the inputs to the airline scheduling prob- lem are of stochastic nature (Day & Ryan, 1997). The objective of minimizing costs and maximizing revenue in deterministic models usually leads to a very tight sched- ule with very short turn times (Langerman & Ehlers, 1997). In this case, stochastic deviations in the planned times will result in system-wide schedule delays. Research concerning the relationship between airline market shares and schedule punctuality showed the significance of passengers switching between airlines, once they expe- rience unsatisfactory services from an airline (Caulkins et al., 1993; Suzuki, 2000).
This has led to several approaches that aim at developing robust airline schedules that are less susceptible to flight delays (Sherali et al., 2006). The proper choice of schedule buffer time for turnarounds increases the reliability of flight connections at airports (Wu & Caves, 2000; Wu, 2005; Lan et al., 2006). Thus, a quality measure for airline schedules should include profit and its performance in operations.
Until now, the airline scheduling problem has been considered as an isolated planning problem within the airline corporate planning system. This development is supported by the schedule’s central role and its major effect on revenues and costs of the airline. However, a second problem in airline planning that has attracted many researchers is revenue and yield management. Its objective is to maximize revenue by selling as many tickets as possible at the highest price possible.22 Since this problem uses a given airline schedule as input, there is a potential for higher profits if revenue management and scheduling issues are solved simultaneously (Jacobs et al., 2000; Barnhart et al., 2003).
Until now, the flight schedule generation phase has attracted only little attention for optimization models, mainly because of its large complexity. The models that are built to support this phase usually do not consider the availability of resources or the costs and implications of their assignment, and if so, the level of detail is much too low (Yan & Tseng, 2002). Other optimization models incorporating flight schedule generation issues mostly adjust departure times of flights within given time windows. In addition and not limited to the flight schedule generation phase, many solution models are rather simplified, disregarding many practical requirements, and
22More details of this topic can be found for example in Belobaba (1987), Kimes (1989), Smith et al. (1992), Vinod (1995), Weatherford (1998), McGill and Van Ryzin (1999), Subramanian et al. (1999), Belobaba and Farkas (1999), Pak and Piersma (2002), Barnhart et al. (2003), Cote et al. (2003), Pulugurtha and Nambisan (2003).
include assumptions that do not represent reality (for example, a monopoly situa- tion, uniformly distributed demand, only one fleet type, a pure (one) hub-and-spoke network, no maintenance capacity constraints etc.). Thus, there is a need for opti- mization models of sufficient detail and scope that capture the critical interactions among the various resources of the airline, its competitors, and airports to support this airline scheduling phase (Yan & Tseng, 2002; Barnhart et al., 2003).
The number of subproblems integrated in one model and the intensity of integra- tion needs to be improved in order to achieve solutions of higher quality (Barnhart et al., 2003; Sherali et al., 2006). Some researchers present integrated models that incorporate some elements of a second problem (major costs or constraints), an iter- ative approach, or an enhanced / advanced but still sequential procedure. Although these models produce (far) better results, regardless of any enhancements, it is be- lieved that no kind of sequential or decomposed solution approach will produce bet- ter or equal solutions to an integrated approach. Only an integrated or simultaneous approach including all subproblems could provide a feasible and optimal solution to the airline scheduling problem (Barnhart & Talluri, 1997; Barnhart et al., 1998;
Cordeau et al., 2001; Klabjan et al., 2002; Barnhart et al., 2003; Cohn & Barnhart, 2003).
To summarize, according to the directions of research efforts, future challenges in airline scheduling can be outlined according to the following objectives (Barnhart et al., 2003):
• improvement of solution quality by reducing randomness in solution approaches,
• incorporating stochastic and uncertain elements in the scheduling process to increase the robustness of the resulting schedules,
• combination of the airline scheduling with revenue management,
• extending the applicability of optimization methods to a larger number of subproblems of the complete airline scheduling problem (like flight schedule generation),
• representing airline operations at a higher level of detail, thus, reducing simpli- fying assumptions and including practical restrictions, and
• relaxing the boundaries between the subproblem in the planning process to- wards an integrated approach.
All these challenges are of major importance to further improve the process of airline scheduling and to obtain realistic schedules that are optimal regarding the air- line’s overall success. However, the research presented in this study focuses on the last three directions, with the last challenge representing one of the most formulated objectives in airline scheduling research.