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Year 2019, Volume: 4 Issue: 1, 1 - 14, 01.01.2019

Abstract

References

  • Adomi, E. E. (2006). Job rotation in Nigerian university libraries. Library Review, 55(1), 66-74. https://doi.org/10.1108/00242530610641808
  • Alliger, G. M., Feinzig, S. L., & Janak, E. A. (1993). Fuzzy sets and personnel selection: Discussion and an application. Journal of Occupational and Organizational Psychology, 66(2), 163-169.
  • Beheshti, H. M., & Lollar, J. G. (2008). Fuzzy logic and performance evaluation: discussion and application. International Journal of Productivity and Performance Management, 57(3), 237-246.
  • Bohanec, M., Urh, B., & Rajkovič, V. (1992). Evaluating options by combined qualitative and quantitative methods. Acta Psychologica, 80(1-3), 67-89.
  • Bossing, N.L. (1955). Orta Dereceli Okullarda Öğretim I-II, (Necmi Sarı, Çev.) İstanbul: Milli Eğitim Basımevi.
  • Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89, 337-347.
  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
  • Byun, H. S., & Lee, K. H. (2005). A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. The International Journal of Advanced Manufacturing Technology, 26(11-12), 1338-1347.
  • Cafoğlu, Z. (1995). Bilgi Çağında Mesleki ve Teknik Eğitimde Toplam Kalite Yönetimi. Mesleki Eğitim Sempozyumu, Elazığ.
  • Canós, L., & Liern, V. (2008). Soft computing-based aggregation methods for human resource management. European Journal of Operational Research, 189(3), 669-681.
  • Celik, M., Kandakoglu, A., & Er, I. D. (2009). Structuring fuzzy integrated multi-stages evaluation model on academic personnel recruitment in MET institutions. Expert Systems with Applications, 36(3), 6918-6927.
  • Chang, D. Y. (1992). Extent analysis and synthetic decision. Optimization techniques and applications, 1(1), 352-355.
  • Chen, C. T. (2001). A fuzzy approach to select the location of the distribution center. Fuzzy sets and systems, 118(1), 65-73.
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
  • Chu, T. C., & Lin, Y. C. (2003). A fuzzy TOPSIS method for robot selection. The International Journal of Advanced Manufacturing Technology, 21(4), 284-290.
  • Churchill, G. A., Ford, N. M. and Walker, O. C. (1990). Sales Force Management: Planning, Implementation and Control, Irwin, USA.
  • Cochran, J. K., & Chen, H. N. (2005). Fuzzy multi-criteria selection of object-oriented simulation software for production system analysis. Computers & operations research, 32(1), 153-168.
  • Cook, R. L. (1992). Expert systems in purchasing: applications and development. Journal of Supply Chain Management, 28(4), 20-27.
  • Dağdeviren, M., Yavuz, S., & Kılınç, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 36(4), 8143-8151.
  • De Korvin, A., Shipley, M. F., & Kleyle, R. (2002). Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects. Journal of Engineering and Technology Management, 19(3-4), 307-319.
  • Delgado, M., Herrera, F., Herrera-Viedma, E., & Martinez, L. (1998). Combining numerical and linguistic information in group decision making. Information Sciences, 107(1-4), 177-194.
  • Dubois, D. J. (1980). Fuzzy sets and systems: theory and applications (Vol. 144). Academic press. New York.
  • Galinec, D., & Vidovic, S. (2006). A theoretical model applying fuzzy logic theory for evaluating personnel in project management. Journal of Behavioral and Applied Management, 7(2), 143-164.
  • Gardiner, L. R., & Armstrong-Wright, D. (2000). Employee selection under anti-discrimination law: implications for multi-criteria group decision support. Journal of Multicriteria Decision Analysis, 9(1-3), 99.
  • Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing, 9(2), 641-646.
  • Herrera, F., & Herrera-Viedma, E. (2000). Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets and systems, 115(1), 67-82.
  • Herrera, F., & Herrera-Viedma, E. (1996). A model of consensus in group decision making under linguistic assessments. Fuzzy sets and Systems, 78(1), 73-87.
  • Jessop, A. (2004). Minimally biased weight determination in personnel selection. European Journal of Operational Research, 153(2), 433-444.
  • Kahraman, C., Ruan, D. and Doğan, İ. (2003). Fuzzy group decision making for facility location selection, Information Sciences, 157, 135-153.
  • Kaufman, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic. New York: Van Nostrand Reinhold Company.Newyork.
  • Karsak, E. E. (2001). Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions. In Multiple criteria decision making in the new millennium (pp. 393-402). Springer, Berlin, Heidelberg.
  • Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). New Jersey: Prentice hall.USA.
  • Liang, G. S., & Wang, M. J. J. (1994). Personnel selection using fuzzy MCDM algorithm. European journal of operational research, 78(1), 22-33.
  • Majozi, T., & Zhu, X. X. (2005). A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants—Personnel evaluation and allocation. Computers & chemical engineering, 29(9), 2029-2047.
  • Negi, D. S. (1989). Fuzzy analysis and optimization. Ph. D. Thesis, Department of Industrial Engineering, Kansas State University.
  • Olorunsola, R. (2000). Job rotation in academic libraries: the situation in a Nigerian university library. Library management, 21(2), 94-98.
  • Parkan, C., & Wu, M. L. (1999). Decision-making and performance measurement models with applications to robot selection. Computers & Industrial Engineering, 36(3), 503-523.
  • Petrovic‐Lazarevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89-105.
  • Rasmy, M. H., Lee, S. M., El-Wahed, W. A., Ragab, A. M., & El-Sherbiny, M. M. (2002). An expert system for multiobjective decision making: application of fuzzy linguistic preferences and goal programming. Fuzzy Sets and Systems, 127(2), 209-220.
  • Oğuzkan, T. (1981). Educational Systems, (Second Publish), İstanbul, Boğaziçi University.
  • Sánchez-Lozano, J. M., García-Cascales, M. S., & Lamata, M. T. (2018). An Analysis of Decision Criteria for the Selection of Military Training Aircrafts. In Soft Computing Based Optimization and Decision Models (pp. 177-190). Springer, Cham.
  • Senge, P.M. (1991). Beşinci Disiplin, (Çev.Ayşegül İldeniz, Ahmet Doğukan), İstanbul, Yapı Kredi Yayınları.
  • Sergaki, A., & Kalaitzakis, K. (2002). A fuzzy knowledge based method for maintenance planning in a power system. Reliability Engineering & System Safety, 77(1), 19-30.
  • Spyridakos, A., Siskos, Y., Yannacopoulos, D., & Skouris, A. (2001). Multicriteria job evaluation for large organizations. European Journal of Operational Research, 130(2), 375-387.
  • Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754.
  • Teodorović, D., & Lučić, P. (1998). A fuzzy set theory approach to the aircrew rostering problem. Fuzzy sets and systems, 95(3), 261-271.
  • Timmermans, D., & Vlek, C. (1992). Multi-attribute decision support and complexity: An evaluation and process analysis of aided versus unaided decision making. Acta Psychologica, 80(1-3), 49-65.
  • Timmermans, D., & Vlek, C. (1996). Effects on decision quality of supporting multi-attribute evaluation in groups. Organizational Behavior and Human Decision Processes, 68(2), 158-170.
  • Yurdakul, M., & İç, Y. T. (2005). Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. International Journal of Production Research, 43(21), 4609-4641.
  • Yurdakul, M., & Çogun, C. (2003). Development of a multi-attribute selection procedure for non-traditional machining processes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(7), 993-1009.
  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-III. Information sciences, 9(1), 43-80.
  • Zimmermann, H. J. (1991). Fuzzy Set Theory and its Applications, second ed. Kluwer Academic Publishers, Boston, Dordrecht, London. 1991.

A New Integrated Fuzzy Multicriteria Approach Towards Evaluation And Selection of Instructor Candidates to Military Schools

Year 2019, Volume: 4 Issue: 1, 1 - 14, 01.01.2019

Abstract

Personnel selection is a critical process for organizations and both quantitative and qualitative factors are used in the decision phase. The criteria should be unique to the organization and the best alternative should be chosen to satisfy requirements. This paper researches the instructor selection process for military academics. The criteria are weighted with fuzzy Analytic Hierarchy Process AHP by experts and candidates are ranked by using fuzzy Technique for Order Preference by Similarity to Ideal Solution TOPSIS Method. The purpose of Fuzzy TOPSIS method, which is one of Multiple Criteria Decision Making MCDM methods, is to allow group decision-making in a fuzzy environment. It involves the calculation of the closeness coefficients by means of Fuzzy Positive Ideal Solution FPIS and Fuzzy Negative Ideal Solution FNIS . Alternatives are ranked according to the calculated closeness coefficients. In the study, candidates were assessed by three DM’s in accordance with seven decision criteria. The decision makers carried out assessments with linguistic variables, and subsequently these variables were transformed into positive trapezoidal fuzzy numbers. The study shows that as a decision tool, the Fuzzy TOPSIS method integrated with Fuzzy AHP is extremely well suited to evaluation and selection decisions regarding candidates for position of instructor.

References

  • Adomi, E. E. (2006). Job rotation in Nigerian university libraries. Library Review, 55(1), 66-74. https://doi.org/10.1108/00242530610641808
  • Alliger, G. M., Feinzig, S. L., & Janak, E. A. (1993). Fuzzy sets and personnel selection: Discussion and an application. Journal of Occupational and Organizational Psychology, 66(2), 163-169.
  • Beheshti, H. M., & Lollar, J. G. (2008). Fuzzy logic and performance evaluation: discussion and application. International Journal of Productivity and Performance Management, 57(3), 237-246.
  • Bohanec, M., Urh, B., & Rajkovič, V. (1992). Evaluating options by combined qualitative and quantitative methods. Acta Psychologica, 80(1-3), 67-89.
  • Bossing, N.L. (1955). Orta Dereceli Okullarda Öğretim I-II, (Necmi Sarı, Çev.) İstanbul: Milli Eğitim Basımevi.
  • Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89, 337-347.
  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
  • Byun, H. S., & Lee, K. H. (2005). A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. The International Journal of Advanced Manufacturing Technology, 26(11-12), 1338-1347.
  • Cafoğlu, Z. (1995). Bilgi Çağında Mesleki ve Teknik Eğitimde Toplam Kalite Yönetimi. Mesleki Eğitim Sempozyumu, Elazığ.
  • Canós, L., & Liern, V. (2008). Soft computing-based aggregation methods for human resource management. European Journal of Operational Research, 189(3), 669-681.
  • Celik, M., Kandakoglu, A., & Er, I. D. (2009). Structuring fuzzy integrated multi-stages evaluation model on academic personnel recruitment in MET institutions. Expert Systems with Applications, 36(3), 6918-6927.
  • Chang, D. Y. (1992). Extent analysis and synthetic decision. Optimization techniques and applications, 1(1), 352-355.
  • Chen, C. T. (2001). A fuzzy approach to select the location of the distribution center. Fuzzy sets and systems, 118(1), 65-73.
  • Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
  • Chu, T. C., & Lin, Y. C. (2003). A fuzzy TOPSIS method for robot selection. The International Journal of Advanced Manufacturing Technology, 21(4), 284-290.
  • Churchill, G. A., Ford, N. M. and Walker, O. C. (1990). Sales Force Management: Planning, Implementation and Control, Irwin, USA.
  • Cochran, J. K., & Chen, H. N. (2005). Fuzzy multi-criteria selection of object-oriented simulation software for production system analysis. Computers & operations research, 32(1), 153-168.
  • Cook, R. L. (1992). Expert systems in purchasing: applications and development. Journal of Supply Chain Management, 28(4), 20-27.
  • Dağdeviren, M., Yavuz, S., & Kılınç, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 36(4), 8143-8151.
  • De Korvin, A., Shipley, M. F., & Kleyle, R. (2002). Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects. Journal of Engineering and Technology Management, 19(3-4), 307-319.
  • Delgado, M., Herrera, F., Herrera-Viedma, E., & Martinez, L. (1998). Combining numerical and linguistic information in group decision making. Information Sciences, 107(1-4), 177-194.
  • Dubois, D. J. (1980). Fuzzy sets and systems: theory and applications (Vol. 144). Academic press. New York.
  • Galinec, D., & Vidovic, S. (2006). A theoretical model applying fuzzy logic theory for evaluating personnel in project management. Journal of Behavioral and Applied Management, 7(2), 143-164.
  • Gardiner, L. R., & Armstrong-Wright, D. (2000). Employee selection under anti-discrimination law: implications for multi-criteria group decision support. Journal of Multicriteria Decision Analysis, 9(1-3), 99.
  • Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing, 9(2), 641-646.
  • Herrera, F., & Herrera-Viedma, E. (2000). Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets and systems, 115(1), 67-82.
  • Herrera, F., & Herrera-Viedma, E. (1996). A model of consensus in group decision making under linguistic assessments. Fuzzy sets and Systems, 78(1), 73-87.
  • Jessop, A. (2004). Minimally biased weight determination in personnel selection. European Journal of Operational Research, 153(2), 433-444.
  • Kahraman, C., Ruan, D. and Doğan, İ. (2003). Fuzzy group decision making for facility location selection, Information Sciences, 157, 135-153.
  • Kaufman, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic. New York: Van Nostrand Reinhold Company.Newyork.
  • Karsak, E. E. (2001). Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions. In Multiple criteria decision making in the new millennium (pp. 393-402). Springer, Berlin, Heidelberg.
  • Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). New Jersey: Prentice hall.USA.
  • Liang, G. S., & Wang, M. J. J. (1994). Personnel selection using fuzzy MCDM algorithm. European journal of operational research, 78(1), 22-33.
  • Majozi, T., & Zhu, X. X. (2005). A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants—Personnel evaluation and allocation. Computers & chemical engineering, 29(9), 2029-2047.
  • Negi, D. S. (1989). Fuzzy analysis and optimization. Ph. D. Thesis, Department of Industrial Engineering, Kansas State University.
  • Olorunsola, R. (2000). Job rotation in academic libraries: the situation in a Nigerian university library. Library management, 21(2), 94-98.
  • Parkan, C., & Wu, M. L. (1999). Decision-making and performance measurement models with applications to robot selection. Computers & Industrial Engineering, 36(3), 503-523.
  • Petrovic‐Lazarevic, S. (2001). Personnel selection fuzzy model. International Transactions in Operational Research, 8(1), 89-105.
  • Rasmy, M. H., Lee, S. M., El-Wahed, W. A., Ragab, A. M., & El-Sherbiny, M. M. (2002). An expert system for multiobjective decision making: application of fuzzy linguistic preferences and goal programming. Fuzzy Sets and Systems, 127(2), 209-220.
  • Oğuzkan, T. (1981). Educational Systems, (Second Publish), İstanbul, Boğaziçi University.
  • Sánchez-Lozano, J. M., García-Cascales, M. S., & Lamata, M. T. (2018). An Analysis of Decision Criteria for the Selection of Military Training Aircrafts. In Soft Computing Based Optimization and Decision Models (pp. 177-190). Springer, Cham.
  • Senge, P.M. (1991). Beşinci Disiplin, (Çev.Ayşegül İldeniz, Ahmet Doğukan), İstanbul, Yapı Kredi Yayınları.
  • Sergaki, A., & Kalaitzakis, K. (2002). A fuzzy knowledge based method for maintenance planning in a power system. Reliability Engineering & System Safety, 77(1), 19-30.
  • Spyridakos, A., Siskos, Y., Yannacopoulos, D., & Skouris, A. (2001). Multicriteria job evaluation for large organizations. European Journal of Operational Research, 130(2), 375-387.
  • Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754.
  • Teodorović, D., & Lučić, P. (1998). A fuzzy set theory approach to the aircrew rostering problem. Fuzzy sets and systems, 95(3), 261-271.
  • Timmermans, D., & Vlek, C. (1992). Multi-attribute decision support and complexity: An evaluation and process analysis of aided versus unaided decision making. Acta Psychologica, 80(1-3), 49-65.
  • Timmermans, D., & Vlek, C. (1996). Effects on decision quality of supporting multi-attribute evaluation in groups. Organizational Behavior and Human Decision Processes, 68(2), 158-170.
  • Yurdakul, M., & İç, Y. T. (2005). Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. International Journal of Production Research, 43(21), 4609-4641.
  • Yurdakul, M., & Çogun, C. (2003). Development of a multi-attribute selection procedure for non-traditional machining processes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(7), 993-1009.
  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-III. Information sciences, 9(1), 43-80.
  • Zimmermann, H. J. (1991). Fuzzy Set Theory and its Applications, second ed. Kluwer Academic Publishers, Boston, Dordrecht, London. 1991.
There are 52 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Mehmet Kabak This is me

Yiğit Kazançoğlu This is me

Mehmet Yüksel

Publication Date January 1, 2019
Published in Issue Year 2019 Volume: 4 Issue: 1

Cite

APA Kabak, M., Kazançoğlu, Y., & Yüksel, M. (2019). A New Integrated Fuzzy Multicriteria Approach Towards Evaluation And Selection of Instructor Candidates to Military Schools. Journal of Learning and Teaching in Digital Age, 4(1), 1-14.

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