Airline Crew Rostering Research Articles

An Improved Honey Badger Algorithm by Genetic Algorithm and Levy Flight Distribution for Solving Airline Crew Rostering Problem

Airline crew rostering problem contains a variety of rules and constraints, and there are almost countless possible scheduling schemes. It is the most complex and important link in the entire crew scheduling plan. In this paper, we build a model that includes qualification constraints. In this paper, we consider two models with qualification constraints with different objective functions, namely minimizing the total cost of the airline and balancing flight utility among pilots as much as possible. To solve this model, the Levy flight is used to improve the ability of the Honey Badger Algorithm (HBA) to jump out of local optima, and the crossover and mutation operators in the Genetic Algorithm (GA) are used to improve the quality of the solution. This improved HBA algorithm significantly improves convergence and solution accuracy. In addition to this, we verified the improved HBA algorithm on 6 instances, of which 4 instances do not contain any qualifications, and 2 instances contain high-qualification flight pairings. The good results of the improved HBA show that it has excellent performance in both objective functions.

Understanding scheduling preferences of airline crews

Airline crew rostering is a complicated planning-type problem and its objective is to assemble pairings into schedules that maximise the satisfaction levels of crews. The rostering process focuses on achieving a more balanced workload distribution among the crewmembers that allows designing rosters in the interest of the crew. The main purpose of this paper is to analyse fundamental aspects of airline crew rostering and its impact on crewmembers’ fatigue. The paper also allows readers to get acquainted with the effects of fatigue on crew’s alertness and performance.

Dynamic evaluation of airline Crew’s flight requests using a neural network

Abstract In airline crew rostering, pilots’ requests to operate specific flights need to be evaluated efficiently to avoid inefficient schedules. Despite the relevance of correctly assessing and granting crew requests, this topic has received very little attention in the literature. In this paper, we address the case this process is a dynamic problem, in which flight requests are submitted while others have already been granted and pre-assigned. This is the first work to dynamically model flight requests during the crew rostering process. We propose a simulation-trained neural-network algorithm to evaluate flight requests, providing a systematic way of assessing flight requests and supporting the definition of a cost-efficient request granting policy. To train and test this algorithm, we developed an innovative rolling rostering framework that captures the dynamic process in practice. The framework relies on an integer linear programming crew rostering model solved with the help of a column-generation algorithm. The neural-network algorithm is trained and tested in a case study with a major European airline. The results show that the algorithm is more effective than the current practice at the airline, granting 22% more requests while using the same workforce to operate the flight schedule.

A Multi-Objective Ant Colony System Algorithm for Airline Crew Rostering Problem With Fairness and Satisfaction

The airline crew rostering problem (CRP) is significant for balancing the workload of crew and for improving the satisfaction rate of crew’s preferences, which is related to the fairness and satisfaction of crew. However, most existing work considers only one objective on fairness or satisfaction. In this study, we propose a new practical model for CRP that takes both fairness and satisfaction into account simultaneously. To solve the multi-objective CRP efficiently, we develop an ant colony system (ACS) algorithm based on the multiple populations for multiple objectives (MPMO) framework, termed multi-objective ACS (MOACS). The main contributions of MOACS lie in three aspects. Firstly, two ant colonies are utilized to optimize fairness and satisfaction objectives, respectively. Secondly, a new hybrid complementary heuristic strategy with three kinds of heuristic information schemes is proposed to avoid ant colonies focusing only on their own objectives. Ant colonies randomly choose one of the three schemes to help explore the Pareto front (PF) sufficiently. Thirdly, a local search strategy with two types of local search respectively for fairness and satisfaction is designed to further approach the global PF. The MOACS is applied to seven real-world monthly CRPs with different sizes from a major North-American airline. Experimental results show that MOACS generally outperforms the greedy algorithm and some other popular multi-objective optimization algorithms, especially on large-scale instances.

Construction-Based Optimization Approaches to Airline Crew Rostering Problem

An airline crew rostering problem (ACRP) is one of the most important problems in an airline planning process. It aims at determining an optimal assignment of pairings, which refer to sequences of flights starting from and ending at the same crew base, to aircrew to form roster lines. In practice, ACRP is subject to various types of constraints. We present a constraint-implicit mathematical model taking into account the basic, horizontal, and vertical constraints. In order to solve a kind of ACRP, we propose a construction-based variable neighborhood search (VNS) framework that can build rosters effectively. Three construction methods, i.e., crew-by-crew, pairing-by-pairing, and orthogonal constructions, are introduced. To evaluate our approaches, we conduct extensive experiments on two scenarios (intense and light workload) of instances originated from a Chinese airline company and make comparisons among different VNS approaches. The computational results show that the proposed approaches are capable of producing high-quality solutions in both scenarios.

Two-level decomposition-based matheuristic for airline crew rostering problems with fair working time

We propose a two-level decomposition-based matheuristic algorithm to solve a practical airline crew rostering problem with fair working time. The goal is to find an optimal assignment of pairings to individual crew members that satisfies hard constraints reflecting, for instance, international flights, rest days and regulatory requirements. The objective is to achieve a fair working time of crew members. We propose a two-level decomposition algorithm applying partial optimization under special intensification conditions (POPMUSIC). The method decomposes the original problem into a master problem and a subproblem. The master problem determines an assignment of pairings and rest days. The subproblem checks the feasibility of the original problem when the solution of the master problem is fixed. These problems are iteratively solved by embedding cuts into the master problem. A new method for solving the master problem by a generalized set partitioning reformulation is proposed. The effectiveness of the proposed method for real-world data is shown via computational experiments.

Using Differential Evolution Method to Solve Crew Rostering Problem

Airline crew rostering is the assignment problem of crew members to planned rotations/pairings for certain month. Airline companies have the monthly task of constructing personalized monthly schedules (roster) for crew members. This problem became more complex and difficult while the aspirations/criterias to assess the quality of roster grew and the constraints increased excessively. This paper proposed the differential evolution (DE) method to solve the airline rostering problem. Different from the common DE, this paper presented random swap as mutation operator. The DE algorithm is proven to be able to find the near optimal solution accurately for the optimization problem. Through numerical experiments with some real datasets, DE showed more competitive results than two other methods, column generation and MOSI (the one used by the Airline). DE produced good results for small and medium datasets, but it still showed reasonable results for large dataset. For large crew rostering problem, we proposed decomposition procedure to solve it in more efficient manner using DE.

Short-Haul Airline Crew Rostering by Using Inequality-Based Multiobjective Genetic Algorithm

A novel method of inequality-based multiobjective genetic algorithm (MMGA) is presented for solving the airline crew rostering problem. This approach combines a traditional genetic algorithm with a multiobjective optimization method to address multiple objectives at the same time and to explore the optimal solution. With the method of inequalities, the objective can be modified to update different requirements. Therefore, the proposed MMGA approach has the advantages of global exploration and robustness. There are numerous local optima in airline crew rostering problems, so the problem is challenging to evaluate by using genetic algorithm-based approaches. Computational experiments show that the proposed method can obtain better and more robust results than those of manual rosters made by rosterers who have many years of experience. The research objective was to solve the complex airline crew rostering problem by using the MMGA approach and to demonstrate that this method can reduce the solution time. The proposed method is successfully applied to a domestic short-haul airline. It helps managers find a high-quality, feasible roster and reduce crew-related costs in crew rostering applications.

Airline Crew Rostering: Problem Types, Modeling, and Optimization

Airline crew rostering is an important part of airline operations and an interesting problem for the application of operations research. The objective is to assign anonymous crew pairings either to personalized rosters or to anonymous bidlines which subsequently will be assigned to individual crew members. Compared to the crew pairing problem, crew rostering has received much less attention in the academic literature and the models presented have been rather simplified. The contribution of this paper is two-fold. First, we want to give a more comprehensive description of real-world airline crew rostering problems and the mathematical models used to capture the various constraints and objectives found in the airline industry. As this has not been attempted in previous research, we think it serves a purpose to reveal the complexity of real-world crew rostering to readers without industrial knowledge of the problem. Second, we want to present the solution methods employed in a commercial crew rostering system, in whose development we both have been involved. The Carmen Crew Rostering system is currently in use at several major European airlines including British Airways, KLM, Iberia, Alitalia, and Scandinavian Airlines (SAS) as well as at one of the world's largest passenger transportation company Deutsche Bahn (German State Railways). During the development of the Carmen Crew Rostering system, we have gained valuable experience about practical problem solving and we think the system constitutes an interesting case in the application of operations research.