Use And Comparison of Topis And Electre Methods In Personnel Selection

Şenel, Bilgin; Şenel, Mine; aydemir, gizem

doi:10.1051/itmconf/20182201021

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…Hybrid MCDM is widely used by many researchers to find the best solution in their prospect [28][29][30][31]. For example in [32], compared to the ELECTRE approach, the TOPSIS method provides more accurate and trustworthy findings in personnel selection. Comparison between the MCDM method also been used in [33] to the proposed hybrid fuzzy DEMATEL-AEW-FVIKOR method shows its advantage in flexibility concerning the decision makers' preference.…”

Section: Hybrid Mcdmmentioning

confidence: 99%

“…MCDM approaches fit different types of decision situations. While CFPR is purposely used for simplifying the pairwise comparison [21] and ELECTRE [32], is used when comparing binary, superiority between different decision points for each rating factor is employed. Many new users have difficulty identifying which type of MCDM technique is best for their particular situation.…”

Section: Hybrid Mcdmmentioning

confidence: 99%

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A Sensitivity Analysis for Roles Selection in Hybrid Multi-Criteria Decision Making

Yusof

Romli

Arshah

et al. 2023

ARASET

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The process of retaining leadership succession at higher education institutions (HEI) is crucial since it has entailed the process of selecting the ideal candidate. The goal is to guide universities toward maintaining organizations' excellence in their academic leadership and management (ALM) positions. Due to the lack of established standards for assessing the competency of possible successors at their home institutions, many ALM of Malaysia HEIs is difficult to identify the proper replacement for their posts. This study aims to propose a multi-criteria tacit knowledge acquisition framework (MC-TKAF) for supporting talent development intervention programs in Malaysia HEIs. It will be based on cognitive apprenticeship, socialization, and informal learning theory which is mostly used in acquiring knowledge from expertise to overcome talent bottlenecks among novices. Fuzzy Delphi will be used as the primary methodology in this study to gather agreement regarding the appropriate indicator to measure tacit knowledge competency among ALMs at Malaysian HEIs. There are three phases: Phase 1 involves analysing the current tacit knowledge acquisition (TKA) and identifying the appropriate parameters to build the intended framework. Phase 2 involves using the results of Phase 1 to create a new framework of tacit knowledge acquisition (TKAF) that is appropriate for the HEI environment. Phase 3's final objective is to assess the viability of the Talent Development Intervention Program's (TDIP) Tacit Knowledge Acquisition Framework (TKAF) utilizing the Multi-Criteria Decision Making (MCDM) approach. This paper's goal is to offer the hybrid MCDM approach as a talent performance indicator for the multi-criteria tacit acquisition framework. The final Phase 3 of the study design will essentially be the subject of this paper. The built-in indicators in this document may be utilized as a guide for the HEI sectors to create talent performance metrics that are appropriate for each TKA applied.

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Section: Hybrid Mcdmmentioning

confidence: 99%

Section: Hybrid Mcdmmentioning

confidence: 99%

A Sensitivity Analysis for Roles Selection in Hybrid Multi-Criteria Decision Making

Yusof

Romli

Arshah

et al. 2023

ARASET

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“…In real life, personnel selection is a Multi-Criteria Decision Making (MCDM) problem, and from the MCDM perspective, it has attracted the attention of many researchers [14]. Jasemi et al [15] used a new fuzzy ELimination Et Choix Traduisant la REalité (ELECTRE) method for personnel selection.…”

Section: Introductionmentioning

confidence: 99%

“…Compute the Euclidean distance between positive (D * i ) and negative (D − i ) ideal solution by applying Equations (14) and (15), as presented in Table 21.…”

mentioning

confidence: 99%

A Bipolar Neutrosophic Multi Criteria Decision Making Framework for Professional Selection

et al. 2020

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Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity and imprecision. It outlines staff contribution and therefore plays a significant part in human resources administration. Additionally, in the era of the Internet of Things and Big Data (IoTBD), professional selection would face several challenges not only to the safe selection and security but also to make wise and prompt decisions especially in the large-scale candidates and criteria from the Cloud. However, the process of professional selection is often led by experience, which contains vague, ambiguous and uncertain decisions. It is therefore necessary to design an efficient decision-making algorithm, which could be further escalated to IoTBD. In this paper, we propose a new hybrid neutrosophic multi criteria decision making (MCDM) framework that employs a collection of neutrosophic analytical network process (ANP), and order preference by similarity to ideal solution (TOPSIS) under bipolar neutrosophic numbers. The MCDM framework is applied for chief executive officer (CEO) selection in a case study at the Elsewedy Electric Group, Egypt. The proposed approach allows us to assemble individual evaluations of the decision makers and therefore perform accurate personnel selection. The outcomes of the proposed method are compared with those of the related works such as weight sum model (WSM), weight product model (WPM), analytical hierarchy process (AHP), multi-objective optimization based on simple ratio analysis (MOORA) and ANP methods to prove and validate the results.

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“…The differences between the four methods of multi-criteria decision-making (MCDM) used in the research[51][52][53].…”

mentioning

confidence: 99%

Comparison between Multi-Criteria Decision-Making Methods and Evaluating the Quality of Life at Different Spatial Levels

et al. 2021

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Achieving a good urban form has been a problem since the formation of the earliest cities. The tendency of human populations toward living in urban environments and urbanization has made the quality of life more prominent. This article aimed to calculate the quality of life in an objective way. For this purpose, the technique for order preferences by similarity to ideal solution (TOPSIS), vlseKriterijumsk optimizacija kompromisno resenje (VIKOR), simple additive weighted (SAW), and elimination and choice expressing reality (ELECTRE) have been utilized. Quality of life was assessed at three spatial levels. In this regard, socioeconomic, environmental, and accessibility dimensions were considered. As a result, in the first level of comparison, sub-districts in District 6 were ranked higher than that of District 13. On the second level, for District 6, vicinity sub-districts had higher rankings than the center, and for District 13, sub-districts near the center of the city had higher rankings. In the third level, District 6 had a higher quality of life. The results of the comparison between research methods showed that the SAW method performs better in terms of stability. Based on the results of correlation tables, there was a strong and direct relationship between each pair of methods at three spatial levels. In addition, as the study area became smaller, the similarity between the methods increased.

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Use And Comparison of Topis And Electre Methods In Personnel Selection

Cited by 11 publications

References 9 publications

A Sensitivity Analysis for Roles Selection in Hybrid Multi-Criteria Decision Making

A Sensitivity Analysis for Roles Selection in Hybrid Multi-Criteria Decision Making

A Bipolar Neutrosophic Multi Criteria Decision Making Framework for Professional Selection

Comparison between Multi-Criteria Decision-Making Methods and Evaluating the Quality of Life at Different Spatial Levels

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