An Analysis of Job Change Decision Using a Hybrid Mcdm Method: A Comparative Analysis

V. Alpagut Yavuz


This paper investigates the decision process relating to job change which mostly depends on individual’s expectations about a job. Failing to fully understand the factors shaping these expectations leads to dissatisfaction and poor work performance; which produces unwanted consequences for both individuals and businesses. Since job change decision is defined as a multiple criteria decision making (MCDM) problem. This study uses a hybrid approach as a methodology combining fuzzy Analytic Hierarchy Analysis (AHP) and fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for the job change decision of a faculty working in a university. In this approach, while the use of fuzzy AHP method helps determine the weight of the decision criteria; fuzzy TOPSIS enables the evaluation of the alternatives. In order to investigate the methods’ applicability in multiple dimensions of decision problem space, a comparison analysis is conducted with the three methodologies; fuzzy AHP, fuzzy TOPSIS and the proposed hybrid approach (named fuzzy AHP-TOPSIS) in the same decision making context. Four factors are considered for the comparison: adequacy to changes of criteria or alternatives; agility in the decision process; computational complexity; and the number of criteria and alternatives. Analysis shows that three methods achieve the same results. This verifies their robustness and indicates that MCDM methods are viable in job change decisions. However; comparison analysis shows that based on the four factors; the proposed hybrid fuzzy AHP-TOPSIS method provide more consistent results than fuzzy AHP and fuzzy TOPSIS methods. Thus the proposed hybrid fuzzy AHP-TOPSIS method is more appropriate to use on a wide range of job change decision problems.


Fuzzy AHP, Fuzzy TOPSIS, job change decision, multi criteria decision making.

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Abo-Sinna, M. A., & Amer, A. H. (2005). Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems. Applied Mathematics and Computation, 162(1): 243–256.

Alp, S., & Özkan, T. K. (2015). Job choice with multi-criteria decision making approach in a fuzzy environment. International Review of Management and Marketing, 5(3): 165–172.

Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4): 141–164.

Boran, F. E., Genç, S., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5): 493–503.

Boswell, W. R., Roehling, M. V., LePine, M. A., & Moynihan, L. M. (2003). Individual job-choice decisions and the impact of job attributes and recruitment practices: a longitudinal field study. Human Resource Management, 42(1): 23–37.

Breaugh, J. A. (2008). Employee recruitment: current knowledge and important areas for future research. Human Resource Management Review, 18(3): 103–118.

Breaugh, J. A., Macan, T. H., & Grambow, D. M. (2008). Employee Recruitment: Current Knowledge and Directions for Future Research. In G. P. Hodgkinson & J. K. Ford (Eds.), International Review of Industrial and Organizational Psychology. John Wiley & Sons, Ltd. pp. 45–82.

Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3): 233–247.

Chan, F. T. S., & Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35(4): 417–431.

Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3): 649–655.

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1): 1–9.

Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2): 289–301.

Chen, L. H., & Hung, C. C. (2010). An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D. International Journal of Production Research, 48(24): 7483–7506.

Cheng, C. H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2): 343–350.

Cheng, C. H., & Mon, D. L. (1994). Evaluating weapon system by analytical hierarchy process based on fuzzy scales. Fuzzy Sets and Systems, 63(1): 1–10.

Csutora, R., & Buckley, J. J. (2001). Fuzzy hierarchical analysis: the lambda-max method. Fuzzy Sets and System, 120(2): 181–195.

Dubois, D. (2011). The role of fuzzy sets in decision sciences: old techniques and new directions. Fuzzy Sets and Systems, 184(1): 3–28.

Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6): 4324–4330.

Ertuğrul, İ., & Karakaşoğlu, N. (2007). Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. The International Journal of Advanced Manufacturing Technology, 39(7-8): 783–795.

Fields, D., Dingman, M. E., Roman, P. M., & Blum, T. C. (2005). Exploring predictors of alternative job changes. Journal of Occupational and Organizational Psychology, 78(1): 63–82.

Gati, I., & Asher, I. (2001). Prescreening, in-depth exploration, and choice: from decision theory to career counseling practice. The Career Development Quarterly, 50(2): 140–157.

Groysberg, B., & Abrahams, R. (2010). Managing yourself: five ways to bungle a job change. Harvard Business Review, January–February. Retrieved January 16, 2015, from

Gumus, A. T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications, 36(2): 4067–4074.

Hadi-Vencheh, A., & Mohamadghasemi, A. (2011). A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification. Expert Systems with Applications, 38(4): 3346–3352.

Halaby, C. N. (1988). Action and information in the job mobility process: the search decision. American Sociological Review, 53(1): 9-25.

Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications : A State-Of-The-Art Survey. Berlin; New York: Springer-Verlag.

Jahanshahloo, G. R., Lotfi, F. H., & Izadikhah, M. (2006). Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Mathematics and Computation, 181(2): 1544–1551.

Jing, L., Chen, B., Zhang, B., Li, P. (2013). A hybrid stochastic-interval analytic hierarchy process approach for prioritizing the strategies of reusing treated wastewater. Mathematical Problems in Engineering, 2013: 1-10., 10.1155/2013/874805

Kahraman, C. (2008). Multi-criteria decision making methods and fuzzy sets. In C. Kahraman (Ed.), Fuzzy Multi-Criteria Decision Making. Springer US pp. 1–18. http://

Kahraman, C., Ateş, N. Y., Çevik, S., Gülbay, M., & Erdoğan, S. A. (2007). Hierarchical fuzzy TOPSIS model for selection among logistics information technologies. Journal of Enterprise Information Management, 20(2): 143–168.

Kilic, H. S. (2013). An integrated approach for supplier selection in multi-item/multi-supplier environment. Applied Mathematical Modelling, 37(14–15): 7752–7763.

Lee, Y. C., Li, M. L., Yen, T. M., & Huang, T. H. (2011). Analysis of fuzzy decision making trial and evaluation laboratory on technology acceptance model. Expert Systems with Applications, 38(12): 14407–14416.

Lima-Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21(August): 194–209.

Lin, C. J., & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1): 205–213.

Liou, J. J. H., & Tzeng, G. H. (2012). Comments on “multiple criteria decision making (MCDM) methods in economics: an overview.” Technological and Economic Development of Economy, 18(4): 672–695.

Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications – two decades review from 1994 to 2014. Expert Systems with Applications, 42(8): 4126–4148.

Montazer, G. A., Saremi, H. Q., & Ramezani, M. (2009). Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection. Expert Systems with Applications, 36(8): 10837–10847.

Murtagh, N., Lopes, P. N., & Lyons, E. (2011). Decision making in voluntary career change: an other-than-rational perspective. Career Development Quarterly, 59(3): 249–263.

Önüt, S., Kara, S. S., & Efendigil, T. (2008). A hybrid fuzzy MCDM approach to machine tool selection. Journal of Intelligent Manufacturing, 19(4): 443–453.

Paksoy, T., Pehlivan, N. Y., & Kahraman, C. (2012). Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Systems with Applications, 39(3): 2822–2841.

Pitz, G. F., & Harren, V. A. (1980). An analysis of career decision making from the point of view of information processing and decision theory. Journal of Vocational Behavior, 16(3): 320–342.

Rajput, H. C., Milani, A. S., & Labun, A. (2011). Including time dependency and ANOVA in decision-making using the revised fuzzy AHP: A case study on wafer fabrication process selection. Applied Soft Computing, 11(8): 5099–5109.

Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill, NewYork.

Tuzkaya, G., Gülsün, B., Kahraman, C., & Özgen, D. (2010). An integrated fuzzy multi-criteria decision making methodology for material handling equipment selection problem and an application. Expert Systems with Applications, 37(4): 2853–2863.

U.S. Bureau of Labor Statistics. (2015). Number of jobs held, labor market activity, and earnings growth among the youngest baby boomers: results from a longitudinal survey (News Release No. USDL-15-0528) p. 12. Retrieved January 25, 2016, from

Vahdani, B., & Hadipour, H. (2010). Extension of the ELECTRE method based on interval-valued fuzzy sets. Soft Computing, 15(3): 569–579.

van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3): 199–227.

Vasant, P., Bhattacharya, A., & Abraham, A. (2008). Measurement of level-of-satisfaction of decision maker in intelligent fuzzy-MCDM theory: a generalized approach. In C. Kahraman (Ed.), Fuzzy Multi-Criteria Decision Making. Springer US pp. 235–261. http://

Wang, Y. J., & Lee, H. S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers & Mathematics with Applications, 53(11): 1762–1772.

Wang, Y. M., & Elhag, T. M. S. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Systems with Applications, 31(2): 309–319.

Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186(2): 735–747.

Yavuz, V. A., Pilli, R., & Pasham, P. R. (2014). A fuzzy analytic hierarchy process model for the evaluation of print advertisement designs. In K. O. Oruç & H. Demirgil (Eds.),. Proceedings of the 15th International Symposium on Econometrics, Operations Research and Statistics, Isparta, Turkey pp. 624–643.

Yazdani-Chamzini, A. (2014). An integrated fuzzy multi criteria group decision making model for handling equipment selection. Journal of Civil Engineering and Management, 20(5): 660–673.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3): 338–353.

Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with pythagorean fuzzy sets. International Journal of Intelligent Systems, 29(12): 1061–1078.

Zimmermann, H. J. (2001). Fuzzy sets-basic definitions. In Fuzzy Set Theory and Its Applications. Springer Netherlands pp. 11–21. http://



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