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

Bahareh Shafipour-Omrani (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Alireza Rashidi Komijan (Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran)
Seyed Jafar Sadjadi (Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
Kaveh Khalili-Damghani (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Vahidreza Ghezavati (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)

Kybernetes

ISSN: 0368-492X

Article publication date: 6 September 2021

Issue publication date: 30 November 2022

118

Abstract

Purpose

One 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/approach

One 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.

Findings

The 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/value

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Keywords

Citation

Shafipour-Omrani, B., Rashidi Komijan, A., Sadjadi, S.J., Khalili-Damghani, K. and Ghezavati, V. (2022), "A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study", Kybernetes, Vol. 51 No. 12, pp. 3545-3573. https://doi.org/10.1108/K-02-2021-0127

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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