Personnel Selection for Promotion using an Integrated Consistent Fuzzy Preference Relations - Fuzzy Analytic Hierarchy Process Methodology: A Real Case Study
- Personnel Selection,
- Multi Criteria Decision Making (MCDM),
- Consistent Fuzzy Preference Relations (CFPR),
- Fuzzy Analytic Hierarchy Process (FAHP)
Copyright (c) 2020 IOR Press
This work is licensed under a Creative Commons Attribution 4.0 International License.
Personnel selection is an important business process for companies. Training, experience information and personal characteristics are important qualities for employee to be recruited. The most accurate result of the personnel selection is obtained from the qualified personnel by determining the personnel who is most suitable for the job requirements. The basic idea of personnel selection is to choose the best candidate for a job. Personnel selection is crucial in human resources management. A solution to the Multi Criteria Decision Making (MCDM) problem is Personnel selection. The main goal of this paper is to find the best personnel using the integrated Consistent Fuzzy Preference Relations (CFPR) and Fuzzy Analytic Hierarchy Process (FAHP) methodology. CFPR is used to obtain the importance weight of personnel selection criteria (22 sub-criteria are categorized under 5 main criteria). Then, the importance weights of personnel selection criteria are integrated with a FAHP model to prioritize the personnel alternatives. For a case study in Turkey, the ranking of the alternatives (17) is calculated using the integrated CFPR-FAHP model, and the best personnel is selected for promotion. This methodology makes it easier for managers/human resources department to decide on recruitment and personnel promotion. The proposed methodology provides the consistent results owing to the integrated methods. The main contribution in this study is the reduction of judgments for a preference matrix using the proposed methodology. To the authors’ knowledge, this study will be the first to integrate CFPR and FAHP methods for personnel selection.
- Ozdemir Y, Basligil H. (2016) Aircraft selection using fuzzy ANP and the generalized Choquet Integral Method: The Turkish Airlines Case. Journal of Intelligent and Fuzzy Systems 31: 589-600.
- Ozdemir Y, Nalbant KG, Basligil H. (2017) Evaluation of personnel selection criteria using Consistent Fuzzy Preference Relations. International Journal of Management Science 4(6): 76-81.
- Chen CT. (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems 114(1): 1-9.
- Lazarevic SP. (2001) Personnel selection fuzzy model. International Transactions in Operational Research 8: 89-105.
- Golec A, Kahya E. (2007) A fuzzy model for competency-based employee evaluation and selection. Computers and Industrial Engineering 52(1): 143-161.
- Lin HT. (2010) Personnel selection using Analytic Network Process and fuzzy data envelopment analysis approaches. Computers and Industrial Engineering 59(4): 937-944.
- Afshari AR, Mojahed M, Yusuff RM, Hong TS, Ismail MY. (2010) Personnel selection using ELECTRE. Journal of Applied Sciences 10(23): 3068-3075.
- Kelemenis A, Askounis D. (2010) A new TOPSIS based multi-criteria approach to personnel selection. Expert Systems with Applications 37(7): 4999-5008.
- Rashidi A, Jazebi F, Brilakis I. (2011) Neurofuzzy Genetic system for selection of construction project managers. Journal of Construction Engineering and Management 137: 17-29.
- Boran FE, Genç S, Akay D. (2011) Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries 21: 493-503.
- Kabak M, Burmaoğlu S, Kazançoğlu Y. (2012) A fuzzy hybrid MCDM approach for professional selection. Expert Systems with Applications 39(3): 3516-3525.
- Baležentis A, Baležentis T, Brauers WK. (2012) Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Systems with Applications 39(9): 7961-7967.
- Rouyendegh BD, Erkan TE. (2012a) An application of the fuzzy Electre method for academic staff selection. Human Factors and Ergonomics in Manufacturing & Service Industries 23(2): 107-115.
- Roy B, Misra SK. (2012) An integrated DEMATEL and AHP approach for personnel estimation. International Journal of Computer Science and Information Technology & Security 2(6): 1206-1212.
- Yu D, Zhang W, Xu Y. (2013) Group decision making under hesitant fuzzy environment with application to personnel evaluation. Knowledge Based Systems 52: 1-10.
- Md Saad R, Ahmad MZ, Abu MS, Jusoh MS. (2014) Hamming Distance Method with subjective and objective weights for personnel selection. The Scientific World Journal ID 865495, doi: 10.1155/2014/865495.
- Aggarwal R. (2014) Identifying and prioritizing human capital measurement indicators for personnel selection using fuzzy MADM, In: Pant M., Deep K., Nagar A., Bansal J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing, Springer, New Delhi 258: 427-439.
- Violeta K, Turskis Z. (2014) A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer. Journal of Business Economics and Management 15(2): 232-252.
- Karabašević D, Stanujkic D, Urosevic S, Maksimovic M. (2015) Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA Methods. Acta Montanistica Slovaca 20(2): 116-124.
- Herrera-Viedma E, Herrera F, Chiclana F, Luque M. (2004) Some issues on Consistency of Fuzzy Preference Relations. European Journal of Operational Research 154: 98-109.
- Wang TC, Lin YL. Incomplete fuzzy preference relations and their fusion. The fifth International Conference on Machine Learning and Cybernetics (ICMLC2006), 13-16 August 2006; Dalian, China, 3: 1823-1828.
- Wang TC, Chen YH. (2007) Applying consistent fuzzy preference relations to partnership selection. Omega 35(4): 384–388.
- Wang TC, Lin YL. (2009) Applying the Consistent Fuzzy Preference Relations to select merger strategy for commercial banks in new financial environments. Expert Systems with Applications 36: 7019-7026.
- Chen YH, Chao RJ. (2012) Supplier selection using consistent fuzzy preference relations. Expert Systems with Applications 39(3): 3233-3240.
- Lu ST, Yu SH. (2012) Risk factors assessment for software development project based on fuzzy decision making. International Journal of Information and Electronics Engineering 2(4).
- Chang TH, Hsu SC, Wang TC. (2013) A proposed model for measuring the aggregative risk degree of implementing an RFID digital campus system with the Consistent Fuzzy Preference Relations. Applied Mathematical Modelling 37: 2605-2622.
- Jafarnejad A, Ebrahimi M, Abbaszadeh MA, Abtahi SM. (2014) Risk management in suppy chain using Consistent Fuzzy Preference Relations. International Journal of Academic Research in Business and Social Sciences 4(1): 77-89.
- Chiu CY, Lin YH, Wang PY, Kuo YW, Chou ST. The study of parameter optimization for screen printing using Consistent Fuzzy reference Relations and Taguchi methods. The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2016).
- Nassar K, Thabet W, Beliveau Y. (2003) A procedure for multi-criteria selection of building assemblies. Automation in Construction 12: 543-560.
- Shapira A, Goldenberg M. (2005) AHP-based equipment selection model for construction projects. Journal of Construction Engineering and Management 131(12): 1263-1273.
- Bitarafan M, Hashemkhani Zolfani S, Arefi SL, Zavadskas EK. (2012) Evaluating the construction methods of cold-formed steel structures in reconstructing the areas damaged in natural crises, using the methods AHP and COPRAS-G. Archives of Civil and Mechanical Engineering 12: 360-367.
- Hsieh TY, Lu ST, Tzeng GH. (2004) Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management 22: 573-584.
- Laarhoven PJM, Pedrycz W. (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems 11: 229-241.
- Buckley JJ. (1985a) Ranking alternatives using fuzzy members. Fuzzy Sets and System 15: 21-31.
- Buckley JJ. (1985b) Fuzzy hierarchical analysis. Fuzzy Sets and Systems 17: 233-247.
- Boender CGE, De Graan JG, Lootsma FA. (1989) Multicriteria decision analysis with fuzzy pairwise comparisons. Fuzzy Sets and Systems 29: 133-143.
- Chang DY. (1996) Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research 95: 649-655.
- Ribeiro RA. (1996) Fuzzy multiple criterion decision making: A review and new preference elicitation techniques. Fuzzy Sets and Systems 78: 155-181.
- Lootsma F. (1997) Fuzzy logic for planning and decision-making. Dordrecht, Kluwer.
- Buyukozkan G, Feyzioglu O. (2004) A fuzzy-logic-based decision-making approach for new product development. International Journal of Production Economics 90: 27-45.
- Chutima P, Suwanfuji P. (1998) Fuzzy Analytical Hierarchy Process part routing in FMS. Thammasat International Journal of Science and Technology 3(2): 29-47.
- Dagdeviren M, Yüksel I. (2008) Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Information Sciences 178: 1717-1733.
- Cebeci U. (2009) Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Systems with Applications 36: 8900-8909.
- Dagdeviren M, Yavuz S, Kilinç N. (2009) Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications 36: 8143-8151.
- Kahraman C, Cebeci U, Ruan D. (2004) Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics 87(2): 171-184.
- Mikhailov L, Tsvetinov P. (2004) Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing 5(1): 23–33.
- Rodríguez A, Ortega F, Concepción R. (2013) A method for the selection of customized equipment suppliers. Expert Systems with Applications 40(4): 1170-1176.
- Cascales MSG, Lamata MT. (2008) Fuzzy analytical hierarchy process in maintenance problem, In Nguyen NT (eds) IEA/AIE 2008, LNAI 5027, Berlin, Springer-Verlag.
- Alias MA, Hashim SZM, Samsudin S. (2009) Using fuzzy analytic hierarchy process for southern Johor river ranking. International Journal of Advances in Soft Computing and its Applications 1(1): 62-76.
- Zeng J, An M, Smith NJ. (2007) Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management 25: 589-600.
- Pan NF. (2008) Fuzzy AHP approach for selecting the suitable bridge construction method. Automation in Construction 17: 958–965.
- Pan NF. (2009) Selecting an appropriate excavation construction method based on qualitative assessments. Expert Systems with Applications 36: 5481-5490.
- Nieto-Morote A, Ruz-Vila F. (2011) A fuzzy approach to construction project risk assessment. International Journal of Project Management 29: 220-231.
- Kog F, Yaman H. (2014) A meta classification and analysis of contractor selection and prequalification. Procedia Engineering 85: 302-310.
- Taylan O, Bafail AO, Abdulaal RMS, Kabli MR. (2014) Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing 17: 105-116.
- Andric JM, Lu DG. (2016) Risk assessment of bridges under multiple hazards in operation period. Safety Science 83: 80-92.
- Mikhailov L. (2002) Fuzzy analytical approach to partnership selection in formation of virtual enterprises. Omega 30: 393-401.
- Huang LC, Huang KS, Huang HP, Jaw BS. (2004) Applying fuzzy neural network in human resource selection system. ICNC 2007: Proceedings of the Third International Conference on Natural Computation, 169-174.
- Gungor Z, Serhadlioglu G, Kesen SE. (2009) A fuzzy AHP approach to personnel selection problem. Applied Soft Computing Journal 9(2): 641-646.
- Chen PC. (2009) A fuzzy multiple criteria decision making model in employee recruitment. International Journal of Computer Science and Network Security 9(7): 113-117.
- Sun CC. (2010) A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications 37(12): 7745-7754.
- Rouyendegh BD, Erkan TE. (2012b) Selection of academic staff using the Fuzzy Analytic Hierarchy Process (FAHP): A Pilot Study. Tehnicki vjesnik / Technical Gazette 19(4): 923- 929.
- Tuzkaya UR, Onut S. (2008) A fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: A case study. Information Sciences 178: 3133-3146.
- Tuzkaya G, Gulsun B, Kahraman C, Ozgen D. (2010) An integrated fuzzy multi-criteria decision making methodology for material handling equipment selection problem and an application. Expert Systems with Applications 37: 2853-2863.
- Ozdemir S, Ozdemir Y. (2017) Prioritizing store plan alternatives produced with shape grammar using multi-criteria decision-making techniques. Environment and Planning B: Urban Analytics and City Science doi: 10.1177/0265813516686566
- Zadeh LA. (1965) Fuzzy sets, Information and Control 8: 338-353.
- Kaya T, Kahraman C. (2011) An integrated fuzzy AHP-ELECTRE methodology for environmental impact assessment. Expert System with Application 38: 8553-8562.