Crew Pairing Research Articles

The Impact of Cruise Controllability on the Decision Making of Schedule Construction

Nowadays, challenged by diverse uncertainties and disruptions (e.g., bad weather), as well as the strict environmental regulations imposed by authorities (e.g., on carbon emissions), airlines are struggling. How to improve their operational efficiency in such a volatile and adverse market becomes a top agenda of airlines. Among various operations, crew scheduling is fundamentally important as staffing cost is a big part of the total operational expenses. It is known that in crew scheduling, “robust crew pairing” is crucial to make the produced pairings less vulnerable in real operations. Existing studies generally construct robustness assuming that the aircraft cruise speed is fixed. However, prior studies have found that flight times exhibit significant variations due to reasons like cruise speed adjustment, and aircraft can control cruise speed for purposes like reducing delays. For crew, cruise speed controllability is also useful in hedging disruptions. For example, one flight can speed up to meet its on-time arrival even if it departs late due to crew disruptions. However, the impacts of cruise speed controllability on crew pairing robustness and the related environmental costs are under-explored. We thus propose this preliminary study to explore the possibility to use cruise speed controllability to enhance schedule robustness for crews.

A roommate problem and room allocation in dormitories using mathematical modeling and multi-attribute decision-making techniques

PurposeIn recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.Design/methodology/approachIn this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.FindingsThe results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.Originality/valueThis novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

An adaptive greedy heuristic for large scale airline crew pairing problems

A crew pairing represents a sequence of flight legs that constitute a crew work allocation, starting and ending at the same crew base. A complete set of crew pairings covers all flight legs in the timetable of an airline for a given planning horizon. That determines the rosters for the crew and their quality, since those pairings would potentially include layovers, deadheads and connection times which are the key factors which directly contribute to the operational crew costs. Considering that crew costs form the second largest bit in the overall operational cost, generating optimized crew pairings is a vital process for the airline companies. In this study, a score-based adaptive greedy heuristic and a genetic algorithm are presented for solving large scale instances of airline crew pairing problems. Both solution methods are applied to a set of real-world problem instances from Turkish Airlines which is one of the largest carriers in the world. The empirical results show that the proposed approaches are indeed capable of generating high quality solutions for crew pairing, even for the large scale problem instances.

Game-theoretic analysis of the impact of crew overnight hotel cost on airlines’ fleet assignment and crew pairing

Crew costs account for a high proportion of the total operational costs of an airline. The cost of overnight stay for the crew at a hotel is an important additional cost. To address this problem, this study establishes an integrated model to depict the interaction between the fleet assignment problem and the crew pairing problem. Because this cost is largely determined by the price agreed by the agreement hotel group, we further present a Stackelberg price game model to capture the interdependence between the integrated fleet assignment and crew pairing of airlines and the price game among hotel groups that provide accommodation services for airlines. We combine the idle time re-allocation with a spatio-temporal graph convolutional network to ensure the robustness of crew pairing. The solvability of the model is improved using the Reformulation-Linearization Technique. To obtain the overall Nash equilibrium of the Stackelberg price game model, we design a heuristic algorithm based on a decomposition algorithm and a proximity search algorithm. A case study is made to demonstrate that our robust model is cost-saving for the airline.

Solving a new mathematical model for the integrated cockpit crew pairing and rostering problem by meta-heuristic algorithms under the COVID-19 pandemic

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic Algorithm (GA), Firefly Algorithm (FA), and Particle Swarm Optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes.

Meta-Heuristic Solver with Parallel Genetic Algorithm Framework in Airline Crew Scheduling

Airline crew scheduling is a very important part of the operational planning of commercial airlines, but it is a linear integer programming problem with multi-constraints. Traditionally, the airline crew scheduling problem is determined by solving the crew pairing problem (CPP) and the crew rostering problem (CRP), sequentially. In this paper, we propose a new heuristic solver based on the parallel genetic algorithm and an innovative crew scheduling algorithm, which improves traditional crew scheduling by integrating CPP and CRP into a single problem. The innovative scheduling method includes a global heuristic search and an adjustment for flights and crew so as to realize crew scheduling. The parallel genetic algorithm is used to divide the population into multiple threads for parallel calculation and to optimize the randomly generated flight sequence to maximize the number of flights that meet the crew configuration. Compared with the genetic algorithm, CPLEX and Gurobi, it shows high optimization efficiency, with a time reduction of 16.57–85.82%. The experiment shows that our crew utilization ratio is higher than that for traditional solvers, achieving almost 44 flights per month, with good scalability and stability in both 206 and 13,954 flight datasets, and can better manage airline crew scheduling in times of crew scarcity.

Solving airline crew pairing problems through constraint partitioning

Solving airline crew pairing problems through constraint partitioning

Solving airline crew pairing problems through constraint partitioning

In this paper, a decomposition technique based on constraint partitioning is developed to solve the crew pairing problem (CPP) which has an overriding importance in the airline industry as it determines the crew cost. The method is based on the observation that in large-scale problems, the constraints can be partitioned to some sub-problems which involve special subsets of variables. The resultant structure is called the 'partitioned structure'. Therefore, in the proposed method, first, a feasible solution is generated for a reduced CPP with a 'partitioned structure' through the optimal solutions of its sub-problems. Then, at each step, the feasible solution is improved through adding/removing some pairings to/from it. The proposed algorithm is applied to a case study from the literature as well as some randomly generated test problems. One advantage of the proposed method is finding multiple feasible solutions with lower time than the method used to solve the case. [Submitted: 8 May 2020; Accepted: 29 December 2021]

A Fuzzy Crew Rostering Model Based on Crew Preferences and Seniorities considering Training Courses: A Robust Optimization Approach

Crew scheduling problem is divided into crew pairing problem (CPP) and crew rostering problem (CRP). In this paper, a rostering model is presented to assign crew to pairings in such a way that total weighted preference is maximized. Crew members declare which parings they wish to be assigned and which ones are undesirable for them. A score is calculated in the objective function if a crew member is assigned to his/her preferred pairing, and a penalty is considered if he/she is assigned to an undesirable pairing. Moreover, crew seniorities are considered in calculating total preference. In addition, the model considers standard rules and regulations as well as crew attendance at the required training courses. The model is formulated in such a way that inconsistent crew members are not assigned to a flight. Due to the uncertainty in determining of the seniority weight, this parameter is considered as fuzzy. At the end, the robust counterpart of the nominal model is developed due to the uncertainty of time away from the base (TAFB). In this research, the issue of inconsistent crew in rostering problem is considered for the first time. Moreover, a new scoring mechanism is introduced to calculate desirable and undesirable assignments in the objective function. The proposed CRP is solved using the genetic algorithm (GA), and its performance is verified in comparison with GAMS in some test problems. On average, the optimality gap in GA is only 0.5 percent. Finally, the proposed model is examined with real-world data from Air India Airline. In comparison with the previous research studies, the suggested model (scoring mechanism) reduced the number of undesirable rosters by 61.59%.

Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity

The cabin crew pairing problem is one of the major challenges faced by airlines. Traditionally, multi-class cabin crews are scheduled on a team basis separated by aircraft types (families) as cockpit crews. However, the manpower requirements for cabin crew across aircraft types (families) are heterogeneous, which cannot be handled by the team scheduling approach. Thus, some airlines nowadays are adopting the individual scheduling approach to deal with the flight manpower requirement heterogeneity. Motivated by the emergence of the individual cabin crew pairing practice, we conduct an analytical study which aims at improving manpower utilization while reducing costs by utilizing a new individual cabin crew pairing generation approach. We also mathematically formulate crew substitution in the model which can help hedge against manpower requirement variations. The impacts of the relationship between manpower availability with requirement benchmarks on cabin crew scheduling strategies are investigated to derive deep insights. A column generation based solution approach is developed. Computational experiments based on small-scale real-world collected flight schedules are conducted to test the distinctive features of the proposed individual pairing model. In addition, a series of large hypothetical instances are employed to examine the advantages of the proposed model over the traditional team-based model in terms of manpower utilization improvement and cost reduction through a customized Genetic Algorithm.

Integral Column Generation for Set Partitioning Problems with Side Constraints

The integral column generation algorithm (ICG) was recently introduced to solve set partitioning problems involving a very large number of variables. This primal algorithm generates a sequence of integer solutions with decreasing costs, leading to an optimal or near-optimal solution. ICG combines the well-known column generation algorithm and a primal algorithm called the integral simplex using decomposition algorithm (ISUD). In this paper, we develop a generalized version of ICG, denoted I2CG, that can solve efficiently large-scale set partitioning problems with side constraints. This new algorithm can handle the side constraints in the reduced problem of ISUD, in its complementary problem, or in both components. Computational experiments on instances of the airline crew pairing problem (CPP) and the multidepot vehicle routing problem with time windows show that the latter strategy is the most efficient one and I2CG significantly outperforms basic variants of two popular column generation heuristics, namely, a restricted master heuristic and a diving heuristic. For the largest tested CPP instance with 1,761 constraints, I2CG can produce in less than one hour of computational time more than 500 integer solutions leading to an optimal or near-optimal solution. Summary of Contribution: In this paper, we develop a new integral column generation algorithm that can solve efficiently large-scale set partitioning problems with side constraints. The latter alter the quasi-integrality property needed for primal integral algorithms. The paper adds a methodological contribution remedying this issue. This remedy should, in our opinion, boost the use of primal exact methods, especially in the column generation context. The paper also has a computational contribution. Effectively, computational experiments on instances of the airline crew pairing problem and the multidepot vehicle routing problem with time windows are extensively discussed. We compare the proposed algorithm to basic variants of two popular column generation heuristics.

BI-OBJECTIVE GOAL PROGRAMMING FOR AIRLINE CREW PAIRING

The crew cost constitutes 20% of the direct operating cost in airline operations after the aircraft fuel cost. Effective crew scheduling can save tens of millions of dollars to the airlines and result in low-cost flight tickets for the passengers and improved quality of life for the crew. The crew pairing requires addressing union expectations, company rules, regulations of countries' civil aviation authorities. In this study, bi-objective goal programming is proposed to minimize the number of crew to perform flights and minimize the flight pairings cost while addressing the challenging issues mentioned above. The integer set-covering goal programming model is formulated and solved using GAMS mathematical programming software. The results showed that the bi-objective model could provide significant cost advantages to airlines. The computational experiments have been performed over a set of real data.

Airline OR Innovations Soar During COVID-19 Recovery

The Airline Group of the International Federation of Operational Research Societies (AGIFORS) held four conferences during May to July 2021 that focused on how COVID-19 was impacting and reshaping the airline industry. Dozens of airline representatives from around the world spoke about how fundamental changes in passenger demand and booking patterns are reshaping the airline industry and driving innovation and research needs. Customers are booking much closer to departure and are canceling or exchanging their tickets more frequently than before COVID-19. Volatility in demand has increased as travel restrictions change and borders reopen. Consequently, greater uncertainty in demand forecasts used as inputs to optimization algorithms is motivating the need for new approaches. Revenue management and scheduling departments are innovating how they predict market sizes and exploring ways to use new data sources or historic bookings in forecasting models. Scheduling and operations departments are making many more flight-cancellation and equipment-swap decisions one to three days from flight departure, which is changing the role of recovery planning. New urgency exists within crew to design duties and rosters that are robust to ever-evolving schedules. Across functional areas, the increasing emphasis is to develop integrated solutions that jointly optimize schedules, crew pairings, and crew rosters for demand forecasts that are uncertain at the time rosters are published. This paper describes how these changes are reshaping the airline industry during COVID-19, explains why short lead times for bookings and uncertainty in demand volumes are expected to remain after COVID-19, and describes how the airline industry is innovating and developing new operations research approaches for handling uncertain and volatile demand.

Cockpit crew pairing Pareto optimisation in a budget airline

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

Solving an integrated mathematical model for crew pairing and rostering problems by an ant colony optimisation algorithm

Solving an integrated mathematical model for crew pairing and rostering problems by an ant colony optimisation algorithm

A matheuristic for the robust integrated airline fleet assignment, aircraft routing, and crew pairing problem

We address an integrated airline scheduling problem that combines three airline planning processes: fleet assignment, aircraft routing, and crew pairing. For a given daily flight schedule, the problem requires simultaneously assigning aircraft and crews to each scheduled flight, taking into account aircraft maintenance restrictions and crew work rules. We propose to solve this complex problem of integrated flight planning while taking into account robustness considerations. In this regard, robustness is achieved by restricting tight connections in the schedule and increasing the number of connections where crews follow the aircraft. We formulate the problem using a very large-scale, yet compact, mixed-integer programming model, and we propose a matheuristic consisting of a decomposition approach and a proximity search algorithm. Computational experiments carried out on real instances from a major airline and having up to 14,014 itineraries, 646 flights, and 202 aircraft provide evidence of the proposed approach’s efficacy. In particular, we find that the average deviation from a conservative bound is at most equal to 0.6%.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

A new mathematical model to cover crew pairing and rostering problems simultaneously

Crew scheduling problem includes two separate subproblems, namely, crew pairing and crew rostering problems. Solving these two subproblems in a sequential order may not lead to an optimal solution. This study includes two main novelties. It combines these two subproblems and presents them in a single model. On the other hand, despite previous researches that considered a pairing continuously, the proposed model benefits from the capability of considering one or more days off in a pairing assigned to a crew member. This is extremely useful as it enables the crew to participate in required courses, doing medical checks, etc. Two solution approaches, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to solve the model. Eventually, the performance of the proposed algorithms is evaluated. Both ended to satisfactory results; however, PSO relatively outperformed GA in terms of solution optimality and computational time.

Robust airline crew scheduling with flight flying time variability

The crew pairing problem is one of the most important but challenging tasks for commercial airlines. However, the operation environment of the aviation industry is highly volatile with diverse uncertainties. Flight flying time variability is an important disruption that usually causes deviations of flight departure/arrival times from the schedule. Traditional crew pairing frameworks without considering flight flying time variability can generate pairings that are fragile to flight delays. However, the impact of flight flying time variability on crew pairings is under-explored. In this paper, we propose two robustness enhancement strategies based on the consideration of flight flying time variability (i.e., encouraging deviation-affected-free flights and discouraging deviation-affected flights). Besides, two robustness measurements are developed to construct two novel robust crew pairing models. One is time based while the other is number based. A customized column generation based solution algorithm is proposed. Computational experiments based on real flight schedules show that our new models can greatly enhance solution robustness (e.g., 49.1% more deviation-buffer time) at a price of an acceptable increase in operating costs (e.g., 9.7%) compared with the traditional model. Besides, extreme-delay flights can be completely avoided in the proposed models. Moreover, the solutions obtained from the time-based model show higher resistance against the disruption of flight flying time variability with a lower operating cost than the number-based model.

Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.