Personnel selection based on computing with words and fuzzy MULTIMOORA
Highlights
► The fuzzy MULTIMOORA method was extended to tackle group decision making. ► The fuzzy weighted averaging (FWA) operator was applied for aggregation. ► The case of personnel selection based on linguistic variables is presented.
Introduction
Personnel selection is an important part of human resources management policy in any enterprise. Personnel selection process is aimed at choosing the best candidate to fill the defined vacancy in a company. It determines the input quality of personnel and thus plays an important role in human resource management (Dursun & Karsak, 2010). The ongoing processes of globalization as well as increasing competition require improving the personnel selection process. Many enterprises, however, are not ready to facilitate the vast amount of funds for personnel selection. Hence, it is important to develop new decision-making techniques available for enterprises possessing various technological, financial, and intellectual capacities. Consequently, more and more scientists have analyzed the practice of personnel recruitment (Zavadskas, Turskis, Tamošaitiene, & Marina, 2008). Indeed, the complexity of the personnel selection problem requires the application of multi-criteria decision making (MCDM) methods for robust recruitment. Consequently, MCDM methods were applied in many studies focused on personnel recruitment problems (Dursun and Karsak, 2010, Kelemenis and Askounis, 2010, Kelemenis et al., 2011, Zavadskas et al., 2008, Zhang and Liu, 2011). The latter two sources provide comprehensive reviews of studies on personnel selection problem.
MCDM methods deal with problems of compromise selection of the best solutions from the set of available alternatives according to objectives. Usually neither of the alternatives satisfies all the objectives therefore satisfactory decision is made instead of optimal one. Roy (1996) presented the following pattern of MCDM problems: (1) α choosing problem – choosing the best alternative; (2) β sorting problem – classifying alternatives into relatively homogenous groups; (3) γ ranking problem – ranking alternatives from best to worst; (4) δ describing problem – describing alternatives in terms of their peculiarities and features. Belton and Stewart (2002) defined the three broad categories of MCDM methods (Løken, 2007): (1) value measurement models; (2) goal, aspiration, and reference level models; (3) outranking models (the French school). In this study we will extend and apply the MULTIMOORA method which encompasses value measurement as well as reference level methods.
The Multi-Objective Optimization by Ratio Analysis (MOORA) was introduced by Brauers and Zavadskas (2006). Subsequently, these authors further developed the method (Brauers & Zavadskas, 2010a) thus presenting the MULTIMOORA (MOORA plus the full multiplicative form). Numerous examples of application of MULTIMOORA are present. The MULTIMOORA was applied in manufacturing and engineering environment (Brauers et al., 2008a, Brauers et al., 2008b, Chakraborty, 2010, Kalibatas and Turskis, 2008, Kracka et al., 2010), as well as regional development studies (Baležentis et al., 2010, Brauers and Ginevičius, 2010, Brauers and Ginevičius, 2009, Brauers et al., 2010, Brauers et al., 2007, Brauers and Zavadskas, 2010b, Brauers and Zavadskas, 2011b). The theory of dominance (Brauers & Zavadskas, 2011a) enables to summarize the ranks obtained from different parts of MULTIMOORA. Moreover, the MULTIMOORA has been updated with fuzzy number theory (Brauers, Baležentis, & Baležentis, 2011).
Zadeh (1965), the Founder of fuzzy logic, proposed employing the fuzzy set theory as a modeling tool for complex systems that are hard to define exactly in crisp numbers. Fuzzy logic hence allows coping with vague, imprecise and ambiguous input and knowledge (Kahraman, 2008, Kahraman and Kaya, 2010). Linguistic reasoning relying on fuzzy logics was introduced by Zadeh, 1975a, Zadeh, 1975b, Zadeh, 1975c and applied in many studies (Chen, 2000, Chou et al., 2008, Liang, 1999, Torlak et al., 2011).
This paper hence is aimed at extending the fuzzy MULTIMOORA for linguistic reasoning under group decision making. The extended fuzzy MULTIMOORA was applied for solving a personnel selection problem. The rest of paper therefore is organized in the following way. Section 2 describes the basics of fuzzy number theory, linguistic reasoning, and the MULTIMOORA. The following Section 3 presents the extended fuzzy MULTIMOORA for group decision making. Finally, Section 4 brings forward the numerical example of personnel selection exercise.
Section snippets
The fuzzy set theory and triangular fuzzy numbers
Fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or non-membership, while fuzzy sets allow partial membership. The theoretical fundamentals of fuzzy set theory are overviewed by Chen (2000).
In a universe of discourse X, a fuzzy subset of X is defined with a membership function which maps each element x ∈ X to a real number in the interval [0; 1]. The function value
The fuzzy MULTIMOORA method for group decision making
The fuzzy MULTIMOORA was introduced by Brauers et al. (2011). However, the fuzzy method is further modified in this study. The fuzzy MULTIMOORA for group decision making (MULTIMOORA–FG) begins with decision matrices , where denotes ith alternative of the jth objective evaluated by the kth decision maker (i = 1, 2, … , m; j = 1, 2, … , n; and k = 1, 2, … , K). Noteworthy, these variables can represent both quantitative and qualitative assessments of alternatives. Then the
Personnel selection: an empirical application
A personnel selection problem will illustrate the group decision-making procedure according to MULTIMOORA–FG. The enterprise has formed an executive committee consisting of four decision-makers (DM1, DM2, DM3, and DM4). The committee is about to choose the best candidate from another four participants (A1, A2, A3, and A4) to fill the vacancy. The committee has decided to consider the following eight attributes: (1) Creativity, innovation (C1); (2) Leadership (C2); (3) Strategic planning (C3);
Conclusion
Personnel selection is an important part of human resources management policy in any enterprise. Personnel selection process is aimed at choosing the best candidate to fill the defined vacancy in a company. It determines the input quality of personnel and thus plays an important role in human resource management. Hence, it is important to develop streamlined decision-making techniques available for enterprises possessing various technological, financial, and intellectual capacities. In this
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