An integrated fuzzy QFD-MCDM framework for personnel selection problem

Document Type : Research Note

Authors

Department of Industrial Engineering, Kinikli Campus, Pamukkale University, Kinikli, 20070, Denizli, Turkey

Abstract

In today’s competitive and high technology world, companies are forced to differentiate themselves with continuous improvement. They need creative, well-educated and self-confident human resource more than ever. Hiring the right person to the right job plays a significant role on firm’s growth. The goal of this paper is to propose a systematic approach for personnel selection problem (PSP) of a textile company in Turkey by considering various performance requirements and criteria. The proposed framework consists of three phases. Initially, Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used for weighting social criteria. Then, weights of technical requirements are calculated by applying Fuzzy Quality Function Deployment (QFD) method allowing to evaluate the interrelationships and correlation of social and technical criteria. Finally, Fuzzy Grey Relational Analysis (GRA) method has been applied to rank the alternatives by considering criteria scores acquired in the previous phase. The method has been illustrated by a case study and compared to the current approach used in the company. The results indicate that this proposed approach can deal with the PSP effectively and help companies to establish a systematic and unbiased way for the problem.

Keywords


  • References

    • Zhang, S-F., and Liu, S-Y. “A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection”, Expert Systems with Applications, 38(9), pp. 11401-11405 (2011).
    • Liao, S. K., and Chang, K. L. “Selecting public relations personnel of hospitals by analytic network process”, Journal of Hospital Marketing and Public Relations, 19(1), pp. 52-63 (2009).
    • Lin, H. T. “Personnel selection using analytic network process and fuzzy data envelopment analysis approaches”, Computers and Industrial Engineering, 59(4), pp. 937-944 (2010).
    • Zadeh, L. A. “Fuzzy sets”, Information and Control, 8(3), pp. 338-353 (1965).
    • Dursun, M., and Karsak, E. E. “A fuzzy MCDM approach for personnel selection”, Expert Systems with Applications, 37(6), pp. 4324-4330 (2010).
    • Afshari, A. R.,Nikolić, M., and Ćoćkalo, D. “Applications of fuzzy decision making for personnel selection problem - A review”, Journal of Engineering Management and Competitiveness, 4(2), pp. 68-77 (2014).
    • Petrovic-Lazarevic, S. “Personnel selection fuzzy model”, International Transactions in Operational Research, 8(1), pp. 89-105 (2001).
    • Özgörmüş, E., Mutlu, Ö., and Güner, H. “Bulanık AHP ile personel seçimi”, 5th Ulusal Üretim Araştırmaları Sempozyumu, Istanbul, Turkey, pp. 111-115 (2005).
    • Güngör, Z., Serhadlioglu, G., and Kesen, S. E. “A fuzzy AHP approach to personnel selection problem”, Applied Soft Computing, 9(2), pp. 641–646 (2009).
    • Chen, P. C. “A fuzzy multiple criteria decision making model in employee recruitment”, International Journal of Computer Science and Network Security, 9(7), pp. 113–117 (2009).
    • Kazan, H., Özçelik, S., and Haykir Hobikoğlu, E. “Election of deputy candidates for nomination with AHP-PROMETHEE methods”, Procedia-Social and Behavioral Sciences, 195, pp. 603-613 (2015).
    • Ayub, M., Kabir, M. J., and Alam, M.G.R. “Personnel selection method using Analytic network process (ANP) and fuzzy concept”, 12th International Conference on Computers and Information Technology, Dhaka, Bangladesh, pp.373-378 (2009).
    • Chen, C. T. “Extensions of the TOPSIS for group decision-making under fuzzy environment”, Fuzzy Sets and Systems, 114(1), pp. 1-9 (2000).
    • Kelemenis, A., and Askounis, D. “A new TOPSIS-based multi-criteria approach to personnel selection”, Expert Systems with Applications, 37(7), pp. 4999-5008 (2010).
    • Boran, F. E., Genç, S., and Akay, D. “Personnel selection based on intuitionistic fuzzy sets”, Human Factors and Ergonomics in Manufacturing and Service Industries, 21(5), pp. 493–503 (2011).
    • Sang, X., Liu, X., and Qin, J. “An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise”, Applied Soft Computing, 30, pp. 190-204 (2015).
    • Hashemi, S. H., Karimi, A., and Tavana, M. “An integrated green supplier selection approach with analytic network process and improved grey relational analysis”, International Journal of Production Economics, 159, pp. 178-191 (2015).
    • Ji, P., Zhang, H. Y. and Wang, J. Q. “A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection”, Neural Computing and Applications, 29(1), pp. 221-234 (2018).
    • Efe, B., and Kurt, M. “A systematic approach for an application of personnel selection in assembly line balancing problem”, International Transactions in Operational Research, 25, pp. 1001-1025 (2018).
    • Urosevic, S., Karabasevic, D., Stanujkic, D., and Maksimovic, M. “An approach to personnel selection in the tourism industry based on the SWARA and the WASPAS methods”, Economic Computation and Economic Cybernetics Studies and Research, 51(1), pp. 75-81 (2017).
    • Karabasevic, D., Zavadskas, E. K., Turskis, Z., and Stanujkic, D. “The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties”, Informatica, 27(1), pp. 49-65 (2016).
    • Chang, K. L. “The use of a hybrid MCDM model for public relations personnel selection”, Informatica, 26(3), pp. 389-406 (2015).
    • Canós, L., Casasús, T., Liern, V., and Pérez, J. C. “Soft computing methods for personnel selection based on the valuation of competences”, International Journal of Intelligent Systems, 29(12), pp. 1079-1099 (2014).
    • Kabak, M. “A Fuzzy DEMATEL-ANP based multi criteria decision making approach for personnel selection”, Journal of Multiple-Valued Logic and Soft Computing, 20(5/6), pp. 571-593 (2013).
    • Baležentis, A., Baležentis, T., and Brauers, W. K. M. “Personnel selection based on computing with words and fuzzy MULTIMOORA”, Expert Systems with Applications, 39(9), pp. 7961-7967 (2012).
    • Dağdeviren, M. “A hybrid multi­criteria decision­making model for personnel selection in manufacturing systems”, Journal of Intelligent Manufacturing, 21(4), pp. 451–460 (2010).
    • Bayram, H., and Sahin, R. “A simulation based multi-attribute group decision making technique with decision constraints”, Applied Soft Computing, 49, pp. 629-640 (2016).
    • Kosareva, N., Zavadskas, E. K., Krylovas, A., and Dadelo, S. “Personnel ranking and selection problem solution by application of KEMIRA method”, International Journal of Computers Communications and Control, 11(1), pp. 51-66 (2016).
    • Liu, H. C., Qin, J. T., Mao, L. X., and Zhang, Z. Y. “Personnel selection using interval 2‐tuple linguistic VIKOR method”, Human Factors and Ergonomics in Manufacturing and Service Industries, 25(3), pp. 370-384 (2015).
    • Bogdanovic, D., and Miletic, S. “Personnel evaluation and selection by multicriteria decision making method”, Economic Computation and Economic Cybernetics Studies and Research, 48(3), pp. 179-196 (2014).
    • Keršulienė, V., and Turskis, Z. “An Integrated Multi-criteria Group Decision Making Process: Selection of the Chief Accountant”, Procedia-Social and Behavioral Sciences, 110, pp. 897-904 (2014).
    • Bali, O. Gümüş, S., and Dağdeviren, M. “A group MADM method for personnel selection problem using Delphi technique based on intuitionistic fuzzy sets”, Journal of Military and Information Science, 1(1), pp. 1–13 (2013).
    • Rouyendegh, B. D., and Erkan T. E. “An application of the fuzzy electre method for academic staff selection”, Human Factors and Ergonomics in Manufacturing & Service Industries, 23(2), pp. 107-115 (2013).
    • Aggarwal, R. “Selection of IT Personnel through Hybrid Multi-attribute AHP-FLP approach”, International Journal of Soft Computing and Engineering, 2(6), pp. 11-17 (2013).
    • Kabak, M., Burmaoğlu, S., and Kazançoğlu, Y. “A fuzzy hybrid MCDM approach for professional selection”, Expert Systems with Applications, 39(3), pp. 3516-3525 (2012).
    • Safarzadegan Gilan, S., Sebt, M. H., and Shahhosseini, V. “Computing with words for hierarchical competency based selection of personnel in construction companies”, Applied Soft Computing, 12(2), pp. 860-871 (2012).
    • Kelemenis, A., Ergazakis, K., and Askounis, D. “Support managers’ selection using an extension of fuzzy TOPSIS”, Expert Systems with Applications, 38(3), pp. 2774-2782 (2011).
    • Gabus, A., and Fontela, E. “World Problems an Invitation to Further Thought within the Framework of DEMATEL”, Battelle Geneva Research Centre, Switzerland (1972).
    • Akyuz, E., and Celik, E. “A fuzzy DEMATEL method to evaluate critical operational hazards during gas freeing process in crude oil tankers”, Journal of Loss Prevention in the Process Industries, 38, pp. 243-253 (2015).
    • Yazdani, M., Chatterjee, P., Zavadskas, E. K., and Zolfani, S. H. “Integrated QFD-MCDM framework for green supplier selection”, Journal of Cleaner Production, 142(4), pp. 3728-3740 (2017).
    • Pamučar, D., Mihajlović, M., Obradović, R., and Atanasković, P. “Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model”, Expert Systems with Applications, 88, pp. 58-80 (2017).
    • Cheng, C., Chen, C., Hsu, F., and Hu, H. “Enhancing service quality improvement strategies of fine-dining restaurants: New insights from integrating a two-phase Decision-making model of IPGA and DEMATEL Analysis”, International Journal of Hospitality Management, 31, pp. 1155– 1166 (2012).
    • Akao, Y. “Development History of Quality Function Deployment - The Customer Driven Approach to Quality Planning and Deployment”, Tokyo, Japan: Asian Productivity Organization (1994).
    • Bottani, E., and Rizzi, A. “Strategic management of logistics service: A fuzzy QFD approach”, International Journal of Production Economics, 103, pp. 585–599 (2006).
    • Bevilacqua, M., Ciarapicab, F. E., and Giacchetta G. “A fuzzy-QFD approach to supplier selection”, Journal of Purchasing & Supply Management, 12(2006), pp. 14-27 (2006).
    • Liu, S., Jeffrey, F., and Yingjie, Y. “A brief introduction to grey systems theory”, Grey Systems: Theory and Application, 2(2), pp. 89-104 (2012).
    • Kuo, M. S., and Liang, G. S. “Combining VIKOR with GRA techniques to evaluate service quality of airports under fuzzy environment”, Expert Systems with Applications, 38, pp. 1304-1312 (2011).
    • Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., and Omid, M. “Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry”, Computers and Operations Research, 89, pp. 337-347 (2018).
    • Pandey, R. K., and Panda, S. S. “Optimization of bone drilling parameters using grey-based fuzzy algorithm”, Measurement, 47, pp. 386-392 (2014).
    • Li, R. J. “Fuzzy method in group decision making”, Computers and Mathematics with Applications, 38(1), pp. 91-101 (1999).
    • Opricovic, S., and Tzeng, G. H. “Defuzzification within a multicriteria decision model”, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 11(5), pp. 635-652 (2003).
    • Yager, R. R. “A procedure for ordering fuzzy subsets of the unit interval”, Information Science, 24, pp. 143–161 (1981).
    • Kulak, O., and Kahraman, C. “Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process”, Information Sciences, 170, pp. 191-210 (2005).