Novel search space updating heuristics-based genetic algorithm for optimizing medium-scale airline crew pairing problems

Demirel, Nihan Çetin; Deveci, Muhammet

doi:10.2991/ijcis.2017.10.1.72

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…Similarly, many studies have also been conducted using meta-heuristics algorithms recently. On the problem of airline crew pairing, first, a novel search space was obtained by an improved dynamic-based genetic algorithm, a deadhead-minimizing pairing search and a partial solution approach (Demirel & Deveci, 2017 ). Then, the memetic and hill climbing-based GAM methodologies were used for optimizing airline crew pairing subject to formation and matching problems (Deveci & Demirel, 2018 ).…”

Section: Background and The Related Literaturementioning

confidence: 99%

Development of a new personalized staff-scheduling method with a work-life balance perspective: case of a hospital

Koruca

Emek

Gulmez³

2023

Ann Oper Res

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Burnout rates and dissatisfaction among healthcare workers remain high due to long working hours. One possible solution to this problem is to let them choose their weekly working hours and starting times in order to achieve a work-life balance. Moreover, a scheduling process that responds to changing healthcare demands at different times of the day should increase work efficiency in hospitals. In this study, a methodology and software were developed to schedule hospital personnel, taking into account their preferences regarding working hours and starting time. The software also allows the hospital management to determine the number of staff needed at different times of the day. Three methods and five working-time scenarios characterized by different divisions of working time are proposed to solve the scheduling problem. The Seniority score Priority assignment Method appoints personnel prioritizing seniority, whereas the newly developed Balanced and Fair assignment Method and Genetic Algorithm Method aim for a more nuanced distribution. The methods proposed were applied to physicians in the internal diseases department in a specific hospital. Weekly/monthly scheduling of all employees was carried out with the software. The results of scheduling factoring in work-life balance, and the performances of algorithms are shown for the hospital where the application was trialled.

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Section: Background and The Related Literaturementioning

confidence: 99%

Development of a new personalized staff-scheduling method with a work-life balance perspective: case of a hospital

Koruca

Emek

Gulmez³

2023

Ann Oper Res

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“…According to the results, the new algorithm led to high quality solutions. Demirel and Deveci (2017) proposed a new search space to update GA for a medium-scale CPP. The method was successfully applied in reducing crew accommodation cost in layovers.…”

Section: Literature Reviewmentioning

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

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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.

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“…It simulates the behavior of grey wolves in nature. Since 2014, GWO have been employed successfully in variety of real-world optimization problems such as scheduling problems [Abed-alguni and Alawad, 2021], text mining [Chantar et al, 2019], aerial vehicles path planning [Qu et al, 2020] and others [Erdogan et al, 2021, Deveci and Çetin Demirel, 2018, Çetin Demirel and Deveci, 2017, Akyurt et al, 2021, Sharma et al, 2020. GWO algorithm has several advantages such as it is simple, easy to use, has fewer parameters that need to be tuned, and has an excellent switching mechanism between exploration and exploitation processes while searching for an optimal solution .…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data

Thaher¹,

Awad²,

Aldasht³

et al. 2022

jucs

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Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO). GWO is a recent metaheuristic algorithm that has been widely employed to solve diverse optimization problems. However, GWO mainly follows the search directions toward the leading wolves, making it prone to fall into local optima, especially when dealing with high-dimensional problems, which is the case when dealing with many biological datasets. An enhanced variation of GWO called EGWO, which adapts two enhancements, is introduced to overcome this specific shortcoming. In the first place, the transition parameter concept is incorporated to move GWO from the exploration phase to the exploitation phase. Several adaptive non-linear decreasing formulas are introduced to control the transition parameters. In the second place, a random-based search strategy is exploited to empower diversity during the search process. Two binarization schemes using S-shaped and V-shaped transfer functions are incorporated to map the continuous search space into a binary one for FS. The efficiency of the proposed EGWO is validated on ten high-dimensional low-samples biological data. Our experiments show the promising performance of EGWO compared to the original GWO approach and other state-of-the-art techniques in terms of dimensionality reduction and the enhancement of classification performance.

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Novel search space updating heuristics-based genetic algorithm for optimizing medium-scale airline crew pairing problems

Cited by 15 publications

References 36 publications

Development of a new personalized staff-scheduling method with a work-life balance perspective: case of a hospital

Development of a new personalized staff-scheduling method with a work-life balance perspective: case of a hospital

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

An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data

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