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İNSAN KAYNAKLARI YÖNETİMİNDE PERFORMANS DEĞERLENDİRME İÇİN BİR BULANIK UZMAN SİSTEM GERÇEKLEŞTİRİMİ

Year 2009, Volume: 9 Issue: 2, 837 - 849, 01.05.2009

Serkan Ballı Aybars Uğur Serdar Korukoğlu

Abstract

İşletmeler için insan kaynakları yönetiminde performans değerlendirmesi, problemin belirsiz ve yetersiz bilgiyle oluşturulması yüzünden karmaşık ve zaman alıcı bir işlemdir. Bu zorlukları ortadan kaldırmak için yapay sinir ağları, uzman sistemler, bulanık mantık ve genetik algoritmalar gibi yapay zekâ teknikleri kullanılabilir. Bu çalışmada; bir işletmede çalışanların yıllık performanslarının değerlendirilmesi için bir bulanık uzman sistem geliştirilmiş ve uygulanmıştır. Elde edilen sonuçlar, bulanık uzman sistem ile performans değerlendirmenin tutarlı ve sağlıklı olduğunu göstermiştir

Keywords

Performans değerlendirme yapay zekâ uzman sistem bulanık mantık bulanık çıkarım

References

  • BYUN, D.H. and SUH, E.H. (1994): “Human resource management expert system”, Expert Systems, 11(2): 109–119.
  • DWEIRI, F.T. and KABLAN, M.M. (2006): “Using fuzzy decision making for the evaluation of the project management internal efficiency”, Decision Support Systems, 42(2), 712-726.
  • ERASLAN E. ve ALGÜN, O. (2005): “İdeal Performans Değerlendirme Formu Tasarımında Analitik Hiyerarşi Yöntemi Yaklaşımı”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 20(1): 95-106.
  • KLIR G.J. and YUAN B., (1995): “Fuzzy Sets and Fuzzy Logic: Theory and Application”, Prentice Hall, New Jersey.
  • KESHWANI, D.R., JONES, D.D., MEYER, G.E. and BRAND, R.M., (2008): Rule-based Mamdani-type fuzzy modeling of skin permeability”, Applied Soft Computing, 8(1): 285-294. LIN H.Y.,
  • HSU, P.Y. and SHEEN, G. J. (2007): “A fuzzy-based decision-making procedure for data warehouse system selection”, Expert Systems with Applications, 32(3): 939-953.
  • MEHRABAD, M. S. and BROJENY, M.F. (2007): “The development of an expert system for effective selection and appointment of the jobs applicants in human resource management”, Computers & Industrial Engineering, 53(2): 306-312.
  • NEGNEVITSKY, M., (2005): “Artificial Intelligence: A Guide to Intelligent Systems”, Addison-Wesley, England.
  • ÖZDEMİR, M. S. (2002): “Bir İşletmede Analitik Hiyerarşi Süreci Kullanılarak Performans Değerlendirme Sistemi Tasarımı”, Endüstri Mühendisliği Dergisi, (2): 2-11.
  • ÖZTÜRK, V. ve SÖNMEZ, A.C. (2006): “Değerlendirme sistemleri için melez uzman sistem yaklaşımı”, İstanbul Teknik Üniversitesi Dergisi/D: Mühendislik Serisi, 5(5): 3–14.
  • PEDRYCZ W. and GOMIDE, F., (1998): “An Introduction to Fuzzy Sets: Analysis and Design”, MIT Press, Cambridge
  • . RADOJEVIC, D. and PETROVIC, S. (1997): “A Fuzzy Approach to Preference Structure in Multicriteria Ranking”, International Transactions in Operational Research, 4(5-6): 419-430. RIGG, G.,
  • MORLEY, R. and HEPPLEWHITE, R.T. (2000): “Themis: The objective assessment of CGF performance”, Proceedingsof the 9 th Conference on Computer Generated Forces and Behavioral Representations, Orlando, FL, 18 May, pp. 133-138.
  • SÜNGÜ, A. (2004): “İnsan Kaynakları Yönetiminde Performans Değerleme ve Astların, Performans Değerleme Çalışmalarına Verdikleri Destek ve Güveni Etkileyen Faktörler Üzerine Bir Araştırma”, Yüksek Lisans Tezi, Muğla Üniversitesi. TIDHAR, G., HEINZE, C., GOSS,
  • S., MURRAY, G., APPLA, D. and LLOYD, I., (1999): “Using Intelligent Agents in Military Simulations or Using Agents Intelligently”, American Proceedings of the Eleventh Innovative Applications of Artificial Intelligence Conference, American Association of Artificial Intelligence (AAAI), pp: 829-836.
  • ZADEH, L. A. (1965): “Fuzzy Sets”, Information Control, 8(3): 338–353.
  • ZADEH, L.A.(1983): “The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems”, Fuzzy Sets and Systems, 11(1-3):197-198. ZIMMERMANN, J.-H., (1996): “Fuzzy Set Theory- and Its Applications”, Third Edition, Kluwer Academic Publishers, U.S.A.

IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT

Year 2009, Volume: 9 Issue: 2, 837 - 849, 01.05.2009

Serkan Ballı Aybars Uğur Serdar Korukoğlu

Abstract

Evaluation performance in human resource management is complicated and time-consuming task for companies because of problem is formed with ambiguous and insufficient information. Artificial intelligence techniques like artificial neural networks, expert systems, fuzzy logic and genetic algorithms can be used to overcome these issues. In this work, a fuzzy expert system has been developed and applied for evaluating annual performance of employees in a company. The obtained results proved that the fuzzy expert system is appropriate and consistent for performance evaluation

Keywords

Performance evaluation artificial intelligence expert system fuzzy logic fuzzy inference

References

  • BYUN, D.H. and SUH, E.H. (1994): “Human resource management expert system”, Expert Systems, 11(2): 109–119.
  • DWEIRI, F.T. and KABLAN, M.M. (2006): “Using fuzzy decision making for the evaluation of the project management internal efficiency”, Decision Support Systems, 42(2), 712-726.
  • ERASLAN E. ve ALGÜN, O. (2005): “İdeal Performans Değerlendirme Formu Tasarımında Analitik Hiyerarşi Yöntemi Yaklaşımı”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 20(1): 95-106.
  • KLIR G.J. and YUAN B., (1995): “Fuzzy Sets and Fuzzy Logic: Theory and Application”, Prentice Hall, New Jersey.
  • KESHWANI, D.R., JONES, D.D., MEYER, G.E. and BRAND, R.M., (2008): Rule-based Mamdani-type fuzzy modeling of skin permeability”, Applied Soft Computing, 8(1): 285-294. LIN H.Y.,
  • HSU, P.Y. and SHEEN, G. J. (2007): “A fuzzy-based decision-making procedure for data warehouse system selection”, Expert Systems with Applications, 32(3): 939-953.
  • MEHRABAD, M. S. and BROJENY, M.F. (2007): “The development of an expert system for effective selection and appointment of the jobs applicants in human resource management”, Computers & Industrial Engineering, 53(2): 306-312.
  • NEGNEVITSKY, M., (2005): “Artificial Intelligence: A Guide to Intelligent Systems”, Addison-Wesley, England.
  • ÖZDEMİR, M. S. (2002): “Bir İşletmede Analitik Hiyerarşi Süreci Kullanılarak Performans Değerlendirme Sistemi Tasarımı”, Endüstri Mühendisliği Dergisi, (2): 2-11.
  • ÖZTÜRK, V. ve SÖNMEZ, A.C. (2006): “Değerlendirme sistemleri için melez uzman sistem yaklaşımı”, İstanbul Teknik Üniversitesi Dergisi/D: Mühendislik Serisi, 5(5): 3–14.
  • PEDRYCZ W. and GOMIDE, F., (1998): “An Introduction to Fuzzy Sets: Analysis and Design”, MIT Press, Cambridge
  • . RADOJEVIC, D. and PETROVIC, S. (1997): “A Fuzzy Approach to Preference Structure in Multicriteria Ranking”, International Transactions in Operational Research, 4(5-6): 419-430. RIGG, G.,
  • MORLEY, R. and HEPPLEWHITE, R.T. (2000): “Themis: The objective assessment of CGF performance”, Proceedingsof the 9 th Conference on Computer Generated Forces and Behavioral Representations, Orlando, FL, 18 May, pp. 133-138.
  • SÜNGÜ, A. (2004): “İnsan Kaynakları Yönetiminde Performans Değerleme ve Astların, Performans Değerleme Çalışmalarına Verdikleri Destek ve Güveni Etkileyen Faktörler Üzerine Bir Araştırma”, Yüksek Lisans Tezi, Muğla Üniversitesi. TIDHAR, G., HEINZE, C., GOSS,
  • S., MURRAY, G., APPLA, D. and LLOYD, I., (1999): “Using Intelligent Agents in Military Simulations or Using Agents Intelligently”, American Proceedings of the Eleventh Innovative Applications of Artificial Intelligence Conference, American Association of Artificial Intelligence (AAAI), pp: 829-836.
  • ZADEH, L. A. (1965): “Fuzzy Sets”, Information Control, 8(3): 338–353.
  • ZADEH, L.A.(1983): “The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems”, Fuzzy Sets and Systems, 11(1-3):197-198. ZIMMERMANN, J.-H., (1996): “Fuzzy Set Theory- and Its Applications”, Third Edition, Kluwer Academic Publishers, U.S.A.
There are 17 citations in total.

Details

Other ID JA26DM84BE
Journal Section Research Article
Authors

Serkan Ballı This is me

Aybars Uğur This is me

Serdar Korukoğlu This is me

Publication Date May 1, 2009
Published in Issue Year 2009 Volume: 9 Issue: 2

Cite

APA Ballı, S., Uğur, A., & Korukoğlu, S. (2009). IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT. Ege Academic Review, 9(2), 837-849.
AMA Ballı S, Uğur A, Korukoğlu S. IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT. ear. May 2009;9(2):837-849.
Chicago Ballı, Serkan, Aybars Uğur, and Serdar Korukoğlu. “IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT”. Ege Academic Review 9, no. 2 (May 2009): 837-49.
EndNote Ballı S, Uğur A, Korukoğlu S (May 1, 2009) IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT. Ege Academic Review 9 2 837–849.
IEEE S. Ballı, A. Uğur, and S. Korukoğlu, “IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT”, ear, vol. 9, no. 2, pp. 837–849, 2009.
ISNAD Ballı, Serkan et al. “IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT”. Ege Academic Review 9/2 (May 2009), 837-849.
JAMA Ballı S, Uğur A, Korukoğlu S. IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT. ear. 2009;9:837–849.
MLA Ballı, Serkan et al. “IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT”. Ege Academic Review, vol. 9, no. 2, 2009, pp. 837-49.
Vancouver Ballı S, Uğur A, Korukoğlu S. IMPLEMENTATION OF A FUZZY EXPERT SYSTEM FOR PERFORMANCE EVALUATION IN HUMAN RESOURCE MANAGEMENT. ear. 2009;9(2):837-49.