A new mathematical model for determining optimal workforce planning of pilots in an airline company

Akyurt, İbrahim Zeki; Kuvvetli, Yusuf; Deveci, Muhammet; Garg, Harish; Yuzsever, Mert

doi:10.1007/s40747-021-00386-x

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…Linear programming [22] Mixed integer linear programming [4], [5], [12] Integer programming [31] Goal programming [9], [11] Genetic algorithm [9], [23] System dynamics [8], [23] Discrete-Event Simulation Model [11] Stochastic programming [30] Robust optimization [11] Optimization is often the preferred method for solving the workforce planning issues in cleaning service operations. This is because it can help minimize costs and increase efficiency while ensuring all cleaning tasks are completed on time.…”

Section: Methods Authormentioning

confidence: 99%

“…For instance, MILP is employed in addressing the workforce planning problems that involve task duration as a continuous variable in the constraints, as highlighted by [4,5]. In another study conducted by study [12] demonstrated the effectiveness of MILP in addressing the workforce planning problems that involve pilot holidays as a continuous variable in the constraints. The study showed that MILP was able to produce a highly effective performance in this regard.…”

Section: Methods Authormentioning

confidence: 99%

“…[11] Transportation A pilot can be hired as a first officer or a captain, the company need to determine whether how many persons of pilot need to be hired. [12] A cleaning services operation is one of the popular services in the service industry. This service is provided by a contractor to an individual's or an organization's place or property.…”

Section: Healthcarementioning

confidence: 99%

“…Skill requirements [4], [6], [10], [12], [22], [23], [24], [25], [26], [27] Workforce demand [4], [5], [6], [9], [22], [24] Time windows [4], [5], [27], [28] Cost [4], [8], [29] Budget [5], [27] Experience / Year of services [10], [30] Career advancements [10], [30] Geographical location [25] Physical resources planning [9] Language [25] Potential for retraining [25] Quantity to produce [28] Contract type [5] Workforce retention rates [11] Preference for the working period [4] Maximum tasks assigned to the worker [4] Workforce holidays [12] The table provides a clear overview of the most commonly cited factors in the workforce planning problem by researchers. The results suggest that a significant number of studies prioritize the -skill requirement‖ as a primary constraint in the workforce planning, indicating the importance of having a skilled workforce to meet organizational goals.…”

Section: Factors Authormentioning

confidence: 99%

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Workforce Planning for Cleaning Services Operation using Integer Programming

Yee,

Rahman,

Zaibidi

et al. 2023

IJACSA

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The cleaning services industry in Malaysia faces significant challenges in effectively managing its workforce. Workforce planning, a critical procedure that aligns employee skills with suitable positions at the right time, is becoming increasingly essential across various organizations, including postal delivery and cleaning services. However, the absence of proper workforce planning from management teams has emerged as a primary concern in this sector. This study identifies an opportunity to improve the workforce planning in the cleaning industry by employing an optimization approach that aims to minimize hiring costs. The main objective of this study is to minimize hiring costs in cleaning services operations at a public university in Malaysia. To achieve this, an optimization model based on integer programming was proposed to represent the current situation. Data collection involved interviews and company reports for the purpose of understanding the current conditions comprehensively. Factors influencing hiring costs were meticulously selected, considering the organization's specific situation. Model evaluation was conducted through whatif analysis, which allowed the evaluation of solutions provided by the modified models in three what-if scenarios. The findings indicated that the proposed modified model could assist organizations in improving the workforce planning by optimizing the allocation of resources, reducing hiring costs, and enhancing cleaner performance. This study offers valuable insights for the management of cleaning services, paving the way for more effective and efficient workforce planning practices in the industry.

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Section: Methods Authormentioning

confidence: 99%

Section: Methods Authormentioning

confidence: 99%

Section: Healthcarementioning

confidence: 99%

Section: Factors Authormentioning

confidence: 99%

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Workforce Planning for Cleaning Services Operation using Integer Programming

Yee,

Rahman,

Zaibidi

et al. 2023

IJACSA

View full text Add to dashboard Cite

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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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