A heuristic method for solving airline crew pairing problems

Novianingsih, Khusnul; Hadianti, Rieske

doi:10.1051/matecconf/201820402006

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to build that customized models, it is very vital to understand the problem itself in great detail. This paper aims to examine the characteristics and understand the inner dynamics of the flight crew disruptions in a large airline operation since crew-related problems can be widely seen in disruptions of airlines [7]. Within the scope of this study, data gathered from a large-scale airline company covering thousands of flights between 2018 and 2019 is analyzed, and a common characteristics for flight crew disruption is proposed.…”

Section: Fig 1 Primary Delay Groupsmentioning

confidence: 99%

Spectral Clustering Approximation For Large Scale Crew Disruption Data Of An Airline Company For Intelligent Crew Recovery

Herekoglu,

Kabak

2023

J. Soft. Comput. Decis. Anal.

View full text Add to dashboard Cite

In the airline industry, after fuel costs, the crew costs con- stitute airlines’ second-highest cost items. For this reason, an airline needs to manage the valuable crew resource effi- ciently. Deviations from plans are fact in airline business and fixing deviations from crew schedules that occurred during operations by minimizing the crew-related delays and associated costs is one of the most important opera- tional burdens of airlines. In this context, the analysis of crew disruption data is vital in order to find disruption characteristics. Clustering analysis is one of the key meth- ods for analyzing the disruption characteristics. In this context, although there have been satisfactory studies in the literature and applications in the industry for small and medium-sized airlines, there is no good solution or industry practice for airlines with extensive networks and fleets. This study aims to analyze and categorize large- scale crew disruption data of a European airline. The relationship between categories of crew disruption and variables such as flight and crew types etc., are determined, and the disruption characteristics are revealed. For this purpose, clusters hidden in the large data set are extracted by spectral clustering. Due to the large size of the input data, a new approximation approach for spectral clustering is introduced. With the help of this new approximation approach, spectral clustering techniques are applied within a limited computational power and time frame as most real world scenario require. Even if the data set is gathered from one airline, the characteristics that are derived from the data is representing most of the cases an airline may face today. and will serve as a basis for further estimation and analysis of crew disruption.

show abstract

Section: Fig 1 Primary Delay Groupsmentioning

confidence: 99%

Spectral Clustering Approximation For Large Scale Crew Disruption Data Of An Airline Company For Intelligent Crew Recovery

Herekoglu,

Kabak

2023

J. Soft. Comput. Decis. Anal.

View full text Add to dashboard Cite

show abstract

“…According to the results, the development of DFS was effective in the quality of pairings and cost reduction. A model with the aim of maximizing the number of flights assigned to each pairing was proposed by Novianingsih and Hadianti (2018). They also applied a new heuristic algorithm that avoided optimal local solutions.…”

Section: Crew Pairing Optimizationmentioning

confidence: 99%

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

Shafipour-Omrani

Komijan

Sadjadi

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

A heuristic method for solving airline crew pairing problems

Cited by 2 publications

References 16 publications

Spectral Clustering Approximation For Large Scale Crew Disruption Data Of An Airline Company For Intelligent Crew Recovery

Spectral Clustering Approximation For Large Scale Crew Disruption Data Of An Airline Company For Intelligent Crew Recovery

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

Contact Info

Product

Resources

About