Soft computing-based aggregation methods for human resource management

Dáros, Lourdes Canós; Liern, Vicente

doi:10.1016/j.ejor.2006.01.054

Cited by 93 publications

(66 citation statements)

References 20 publications

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“…For example, in [2] OWA operators are used to aggregate selection criteria in a personnel selection problem, [3] details the use of an OWA to produce an overall indicator of security of energy supply, in [4] performance indicators of small water utilities are combined to produce an overall performance assessment using an OWA operator, [5] describes the use of a fuzzy OWA operator for rating information security products in terms of reduction of information security risk, and in [6] and [7] the authors use modified OWA operators on a financial product selection problem and a football team player selection problem respectively.…”

Section: B Ordered Weighted Averagementioning

confidence: 99%

Evolving OWA operators for cyber security decision making problems

Miller

Garibaldi

Appleby

2013

2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)

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Abstract-Designing secure software systems is a non-trivial task as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, a great deal of expertise is required to assess the security risks posed to a proposed system in its design stage. In this research we demonstrate how Evolutionary Algorithms (EAs) and Simulated Annealing (SA) can be used with Ordered Weighted Average (OWA) operators to provide a suitable aggregation tool for combining experts' opinions of individual components of an specific technical attack to produce an overall rating that can be used to rank attacks in order of salience. A set of thirty nine cyber security experts took part in an exercise in which they independently assessed a realistic system scenario. We show that using EAs and SA, OWA operators can be tuned to produce aggregations that are more stable when applied to a group of experts' ratings than those produced by the arithmetic mean, and that the difference between the solutions found by each of the algorithms is minimal. However, EAs do prove to be a quicker method of search when an equivalent number of evaluations is performed by each method.

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Section: B Ordered Weighted Averagementioning

confidence: 99%

Evolving OWA operators for cyber security decision making problems

Miller

Garibaldi

Appleby

2013

2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)

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“…Yeh (2003) and Cheng (2009, 2009a) used interviewing for criteria selection. (Liang and Wang, 1992) Personnel placement (Liang and Wang, 1994) Personnel Selection (Yaakob and Kawata, 1999) Finding the better worker for production line (Ertugrul Karsak, 2000) Personnel Selection for an expatriate position (Lovrich, 2000) Senior economic and financial analyst selection (Chen, 2000) System analysis engineer selection in a software company • (Karsak, 2001) Personnel Selection (Capaldo and Zollo, 2001) Personnel Selection • (Lazarevic, 2001) Senior economic and financial analyst • (Butkiewicz, 2002) Staff selection in a tourism agency (Huang, et al, 2004) Middle manager Selection • (Drigas, et al, 2004) Unemployed Matching (Chen and Cheng, 2005) IS personnel for the position of a project manager • • (Saghafian and Hejazi, 2005) University professor hiring • • (Wang and Elhag, 2006) System analysis engineer selection in a software company • (Golec and Kahya, 2007) Employee evaluation and Selection • (Baykasoglu, et al, 2007) Project team members Selection (Mahdavi, et al, 2008) System analyst selection in a software company Zavadskas et al, 2008) Project manager selection (Canós and Liern, 2008) Personnel selection (Celik, et al, 2009) Recruitment of senior lecturer • Select an overseas marketing manager • (Güngör, et al, 2009) New position personnel selection Dejiang, 2009) RandD personnel selection (Polychroniou and Giannikos, 2009) Credit officer • (Dursun and Karsak, 2010) Industrial engineer • ( Kelemenis and Askounis, 2010) Top management team member • • (Kelemenis, et al, 2011) Manger selection • • (Boran, et al, 2011) Selecting a sales manager at a manufacturing company • (Abbas Rashidi, et al, 2011) Selection of Construction Project Managers • (Zhang and Liu, 2011) H...…”

Section: Selection Criteriamentioning

confidence: 99%

“…There were ten qualitative criteria for selecting the best candidate amongst five prospective applications. Canós and Liern (2008) combined Fuzzy with ordered weighted average (OWA) aggregation operators for personnel selection problem. They developed a decision support system (DSS) to help managers for selecting decision making.…”

Section: Fuzzy Electrementioning

confidence: 99%

Applications of fuzzy decision making for personnel selection problem: A review

Afshari

2014

JEMC

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Personnel selection determines the input quality of personnel, therefore, plays a decisive role in human resource management. Personnel selection problem has been studied extensively. Selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. The necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy decision making along with their extensions have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Fuzzy decision making methods have become increasingly popular in decision making for personnel selection. Various decision making approaches have been proposed to solve the problem. This paper presents a comprehensive literature review of the applying Fuzzy decision making techniques in personnel selection problem.

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“…For example, [14] uses an OWA in decision making applied to the personnel selection problem. In [15], an OWA is used to aggregate different performance indicators to assess the performance of small drinking water utilities, and [15] uses and OWA to aid in the selection of financial products.…”

Section: Ordered Weighted Averagementioning

confidence: 99%

Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance

2014

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Abstract-Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are generated from real data, and while two different measures may be used to automatically compare such fuzzy sets, it is difficult to interpret two different results. This is especially true where a large number of fuzzy sets are being compared as part of a reasoning system. This paper introduces a method for combining the results of multiple measures into a single measure for the purpose of analysing and comparing fuzzy sets. The combined measure alleviates ambiguous results and aids in the automatic comparison of fuzzy sets. The properties of the combined measure are given, and demonstrations are presented with discussions on the advantages over using a single measure.

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Soft computing-based aggregation methods for human resource management

Cited by 93 publications

References 20 publications

Evolving OWA operators for cyber security decision making problems

Evolving OWA operators for cyber security decision making problems

Applications of fuzzy decision making for personnel selection problem: A review

Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance

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