A systematic approach for an application of personnel selection in assembly line balancing problem

• Author: Mustafa Kurt,Burak Efe
• Published date: 01 May 2018
• Date: 01 May 2018
• DOI: http://doi.org/10.1111/itor.12439

Intl. Trans. in Op. Res. 25 (2018) 1001–1025

DOI: 10.1111/itor.12439

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A systematic approach for an application of personnel selection

in assembly line balancing problem

Burak Efeaand Mustafa Kurtb

aDepartment of Industrial Engineering, Necmettin Erbakan University, Turkey

bDepartment of Industrial Engineering, Gazi University, Turkey

E-mail: burakefe0642@gmail.com [Efe];mkurt@gazi.edu.tr [Kurt]

Received 23 September 2016; receivedin revised form 13 May 2017; accepted 18 May 2017

Abstract

This paper aims to present a novel systematic approach to personnel selection. The contributions of this

paper are on the multicriteria decision-making problem and the assembly line balancing problem related

to personnel selection. This paper presents a novel method, which is the possibility degree based TOPSIS

(technique for order preference by similarity to ideal solution) method with intervaltype-2 trapezoidal fuzzy

(IT2TrF) numbers, as an extension of the TOPSIS method for the personnel selection problem. This paper

presents the possibility mean value and possibility degree measures instead of distance measures in current

TOPSIS to define a comparative index. The closeness coefficient based on the possibility degree of each

alternative is calculated according to the approximate positive and negative ideal solutions. The subjective

judgments of the decision makers are aggregated to calculate the weights of the criteria. The weights based

on the IT2TrF number are input into IT2TrF number based the TOPSIS phase to rank the alternatives. The

proposed method is applied to evaluate the personnel selection in an assembly line of a textile firm after the

closeness coefficient of each personnel is determined.

Keywords:TOPSIS; possibility degree; interval type-2 fuzzy numbers;assembly line balancing problem; personnel selection

1. Introduction

Personnel selection is one of the most important practices of human resources management. This is

the process of selecting the best among the candidates applying for an identified vacancy in the firm

so that the best candidate must meet the requiredproficiencies of the job in the best manner (Zhang

and Liu, 2011). Personnel selection defines the input quality of personnel and thus ensures a key role

in personnel selection and recruitment (Dursun and Karsak, 2010). Many modern organizations

deal with big challenges due to the growing competition in the global market. The skill, capability,

knowledge, and the other abilities of their personnel affect the performance of the organizations

significantly.

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1002 B. Efe and M. Kurt / Intl. Trans. in Op. Res.25 (2018) 1001–1025

Various authors have used many multicriteria decision-making (MCDM) techniques such as

DEMATEL (decision-making trial and evaluation laboratory; Efe and Efe, 2016), PROMETHEE

(preference ranking organization method for enrichment evaluations; Yerlikaya and Arıkan, 2016),

TOPSIS (technique for order preference by similarity to ideal solution; Bayram and S¸ahin, 2016)

in the literature. Some papers related to personnel selection are presented next. Gibney and Shang

(2007) handled the analytical hierarchy process (AHP) to select the best candidate in the per-

sonnel selection problem. Lin (2010) developed the combined analytic network process (ANP)

and fuzzy data envelopment analysis method in the personnel selection problem. Dursun and

Karsak (2010) and Liu et al. (2015) described the fuzzy TOPSIS with 2-tuples and the VIKOR

(VlseKriterijumska Optimizacija I Kompromisno Resenje) with interval 2-tuple linguistic variables

in personnel selection, respectively. Zhang and Liu (2011) introduced an intuitionistic fuzzy mul-

ticriteria group decision making method based on a gray relational analysis to select the best

candidate. Boran et al. (2011) proposed intuitionistic fuzzy TOPSIS approach for personnel se-

lection. Baleˇ

zentis et al. (2012) suggested fuzzy MULTIMOORA under group decision making

in personnel selection problem. Yu et al. (2013) investigated a hesitant fuzzy group decision mak-

ing method with some aggregation operators for personnel evaluation. Sang et al. (2015) pre-

sented Karnik–Mendel algorithm-based fuzzy TOPSIS in personnel selection application. Chang

(2015) presented a hybrid approach, which includes fuzzy Delphi, ANP, and TOPSIS methods,

to select public relations personnel in tourism industry in Taiwan. Ji et al. (2016) proposed to

select the best personnel a projection-based TODIM (an acronym in Portuguese of interactive

and decision-making method named Tomada de decisao interativa e multicrit´

evio) method using

multivalued neutrosophic numbers. Karabasevic (2016) considered step-wise weight assessment ra-

tio analysis and additive ratio assessment methods under uncertainties for selection of candidate

for the vacant position of a sales manager. Qin et al. (2016) developed some hesitant fuzzy ag-

gregation operators based on Frank triangular norms and applied in human resource selection

them. Aarushi (2016) presented an integrated AHP-TOPSIS approach in problem of personnel

selection.

The contributions of this paper are on the MCDM problem and the assembly line balancing

problem related to personnel selection. First, this paper proposes a novel extension of the TOPSIS

method for the MCDM problem related to personnel selection. Use of the TOPSIS method with

possibility theory or the ranking approach to solve MCDM problems has been proposed by some

works. Ye and Li (2014) presented an extended TOPSIS approach based on the possibility theory

by using triangular fuzzy numbers. Our proposed approach considers interval type-2 fuzzy (IT2F)

numbers unlike the method proposed by Ye and Li, as IT2F numbers are able to present more

uncertainties of the subjective judgments in complex problems. Chen and Lee (2010a) and Chen

and Hong (2014) introduced IT2F TOPSIS with a ranking method to overcome the limitations

of type-1 fuzzy numbers. All elements of the upper and lower membership functions in above

papers are not taken into consideration concurrently. Therefore, the result is sometimes illogical.

A number of possibility methods are presented in the literature. Gong et al. (2015) have claimed

that the membership effect of the IT2F number in these papers has been enlarged. Chen (2015)

proposed a likelihood-based comparison method to extend the IT2F TOPSIS. Chen used a complex

possibility method, which seems hard. The approach we present utilizes the possibility mean value

and possibility degree measures proposed by Gong et al. (2015) as a possibility method. This

possibility method provides a simple, useful, and fast evaluation process.

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2017 The Authors.

International Transactionsin Operational Research C

2017 International Federation of OperationalResearch Societies