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DESTEK VEKTÖR REGRESYON VE İKİZ DESTEK VEKTÖR REGRESYON YÖNTEMİ İLE TEDARİKÇİ SEÇİMİ

Yıl 2016, Cilt: 17 Sayı: 2, 241 - 253, 01.07.2016

Öz

Tedarikçi seçimi sorunu son zamanlarda literatürde oldukça ilgi görmektedir. Güncel literatür, yapay zeka tekniklerinin geleneksel istatistiksel yöntemlerle karşılaştırıldığında daha iyi bir performans sağladığını göstermektedir. Son zamanlarda, destek vektör makinesi, araştırmacılar tarafından çok daha fazla ilgi görse de, buna dayalı tedarikçi seçimi çalışmalarına pek sık rastlanmamaktadır. Bu çalışmada, tedarikçi kredi endeksini tahmin etmek amacıyla, destek vektör regresyon DVR ve ikiz destek vektör regresyon İDVR teknikleri kullanılmıştır. Pratikte, tedarikçi verisini içeren örneklemler sayıca oldukça yetersizdir. DVR ve İDVR daha küçük örneklemlerle analiz yapmaya uyarlanabilir. Tedarikçilerin belirlenmesinde DVR ve İDVR yöntemlerinin tahmin kesinlikleri karşılaştırılmıştır. Gerçek örnekler İDVR yönteminin DVR yöntemine kıyasla üstün olduğunu göstermektedir.

Kaynakça

  • Abdollahi, M., Arvan, M. ve Razmi, J. (2015). An integrated approach for supplier portfolio selection: Lean or agile?, Expert Systems with Applications, 42(1), 679-690.
  • Arikan, F. ve Kucukce, Y.S. (2012). A supplier selection-evaluation problem for the purchase action and its solution, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(2), 255-264.
  • Bruno, G. ve Esposito, E., Genovese, A., Passaro, R. (2012). AHP-based approaches for supplier evaluation: Problems and perspectives, Journal of Purchasing and Supply Management, 18(3), 159-172.
  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery, 2(2), 121- 167.
  • Cherkassky, V. ve Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17, 113–126.
  • Choi, T.Y. ve Hartley, J.L. (1996). An exploration of supplier selection practices across the supply chain, Journal of Operations Management, 14(4), 333–343.
  • Choy, K.L. ve Lee, W.B. (2002). A generic tool for the selection and management of supplier relationships in an outsourced manufacturing environment: the application of case based reasoning, Logistics Information Management, 15(4), 235–253.
  • Choy, K.L., Lee, W.B. ve Lo, V. (2003). Design of an intelligent supplier relationship management system: A hybrid case based neural network approach, Expert Systems with Applications, 24(2), 225–237.
  • Choy, K.L., Lee, W.B., Lau, H.C.W., Lu, D. ve Lo, V. (2004). Design of an intelligent supplier relationship management system for new product development, International Journal of Computer Integrated Manufacturing, 17(8), 692–715.
  • Dagdeviren, M. ve Eraslan, E. (2008). Supplier selection using promethee sequencing method, Journal of the Faculty of Engineering and Architecture of Gazi University, 23(1), 69-75.
  • Fung, G. ve Mangasarian, O. (2005). Multicategory proximal support vector machine classifiers, Machine Learning, 59, 77–97.
  • Ghorai, S., Mukherjee, A. ve Dutta, P. (2009). Nonparallel plane proximal classifier, Signal Processing, 89(4), 510–522.
  • Gunes, T. ve Polat, E. (2009). Feature selection in facial expression analysis and its effect on multi-svm classifiers, Journal of The Faculty of Engineering and Architecture of Gazi University, 24(1), 7-14.
  • Guo, X., Yuan, Z. ve Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine, Expert Systems with Applications, 36(3), 6978-6985.
  • Guo, X., Zhu, W. ve Shi, J. (2014). Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection, Expert Systems with Applications, 41(4), 2083-2097.
  • Guosheng, H. ve Guohong, Z. (2008). Comparison on neural networks and support vector machines in suppliers' selection, Journal of Systems Engineering and Electronics, 19(2), 316-320.
  • Hsu, C.-W., Chang, C.-C. ve Lin, C.-J. (2004). A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University.
  • Ince, H. ve Trafalis, T.B. (2007). Kernel principal component analysis and support vector machines for stock price prediction, IIE Transactions, 39(6), 629-637.
  • Ince, H. ve Trafalis, T.B. (2008). Short term forecasting with support vector machines and application to stock price prediction, International Journal of General Systems, 37(6), 677- 687.
  • Jain, V., Wadhwa, S. ve Deshmukh, S.G. (2007). Supplier selection using fuzzy association rules mining approach, International Journal of Production Research, 45(6), 1323–1353.
  • Jayadeva, Khemchandani, R. ve Chandra, S. (2007). Twin support vector machines for pattern classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 905–910.
  • Juang, C.-F. ve Shiu, S.-J. (2008). Using self-organizing fuzzy network with support vector learning for face detection in color images, Neurocomputing, 71(16-18), 3409-3420.
  • Kao, C. ve Liu, S.-T. (2000). Fuzzy efficiency measures in data envelopment analysis, Fuzzy Sets and Systems, 113(3), 427–437.
  • Kar, A.K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network, Journal of Computational Science, 6(1), 23-33.
  • Kim, H.-C., Pang, S., Je, H.-M., Kim, D. ve Bang, S.Y. (2003). Constructing support vector machine ensemble, Pattern Recognition, 36(12), 2757-2767.
  • Kumar, M. ve Gopal, M. (2009). Least squares twin support vector machines for pattern classification, Expert Systems with Applications, 36(4), 7535–7543.
  • Kuo, R.J. ve Lin, Y.J. (2012). Supplier selection using analytic network process and data envelopment analysis, International Journal of Production Research, 50(11), 2852-2863.
  • Kuo, R.J., Hong, S.M., Lin, Y. ve Huang, Y.C. (2008). Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF–THEN rules, Neurocomputing, 71(13-15), 2893–2907.
  • Kuo, R.J., Hong, S.Y. ve Huang, Y.C. (2010). Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection, Applied Mathematical Modelling, 34, 3976–3990.
  • Kuo, R.J., Lee, L.Y. ve Hu, T.-L. (2008). An intelligent decision support system for supplier selection, Proceedings of Tenth International Conference on Enterprise Information Systems, June 12–16, Barcelona – Spain, 241–248.
  • Kuo, R.J., Wang, Y.C. ve Tien, F.C. (2010). Integration of artificial neural network and MADA methods for green supplier selection, Journal of Cleaner Production, 18(12), 1161–1170.
  • Lau, H.C.W., Lee, C.K.M., Ho, G.T.S., Pun, K.F. ve Choy, K.L. (2006). A performance benchmarking system to support supplier selection, International Journal of Business Performance Management, 8(2–3), 132–151.
  • Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X. ve Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method, Expert Systems with Applications, 39(1), 424-430.
  • Lu, C.J., Lee, T.S. ve Chiu, C.C. (2009). Financial time series forecasting using independent component analysis and support vector regression, Decision Support Systems, 47(2), 115- 125.
  • Luo, X., Wu, C., Rosenberg, D. ve Barnes, D. (2009). Supplier selection in agile supply chains: An information-processing model and an illustration, Journal of Purchasing & Supply Management, 15, 249–262.
  • Mohanty, R.P. ve Deshmukh, S.G. (1993). Using of analytic hierarchic process for evaluating sources of supply, International Journal of Physical Distribution & Logistics Management, 23(3), 22–28.
  • Narasimhan, R., Talluri, S. ve Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: an empirical examination, Journal of Supply Chain Management, 37(3), 28–37.
  • Noorul Haq, A. ve Kannan, G. (2006). Design of an integrated supplier selection and multi- echelon distribution inventory model in a built-to-order supply chain environment, International Journal of Production Research, 44(10), 1963–1985.
  • Nydick, R.L. ve Hill, R.P. (1992). Using the analytic hierarchy process to structure the supplier selection procedure, International Journal of Purchasing and Materials Management, 28(2), 31–36.
  • Peng, X. (2010). TSVR: an efficient twin support vector machine for regression, Neural Networks, 23(3), 365–372.
  • Sagiroglu, S., Yolacan, E.N. ve Yavanoglu, U. (2011). Designing and developing an intelligent intrusion detection system, Journal of The Faculty of Engineering and Architecture of Gazi University, 26(2), 325-340.
  • Sun, H.-L., Xie, J.-Y. ve Xue, Y.-F. (2005). An SVM-based model for supplier selectionusing fuzzy and pairwise comparison, In Proceedings of 2005 international conference on machine learning and cybernetics, 6, 3629–3633.
  • Talluri, S. (2002). Enhancing supply decisions through the use of efficient marginal costs models, Journal of Supply Chain Management, 38(4), 4–10.
  • Talluri, S. ve Narasimhan, R. (2005). A note on a methodology for supply base optimization, IEEE Transactions on Engineering Management, 52(1), 130–139.
  • Talluri, S. ve Narasimhan, R. (2003). Vendor Evaluation with performance variability: a max- min approach, European Journal of Operational Research, 146(3), 543–552.
  • Talluri, S., Narasimhan, R. ve Nair, A. (2006). Vendor performance with supply risk: a chance- constrained DEA Approach, International Journal of Production Research, 100(2), 212– 222.
  • Tseng, M.-L., Chiang, J.H. ve Lan, L.W. (2009). Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral, Computers & Industrial Engineering, 57(1), 330-340.
  • Vahdani, B., Iranmanesh, S.H., Meysam Mousavi, S. ve Abdollahzade, M. (2012). A locally linear neuro-fuzzy model for supplier selection in cosmetics industry, Applied Mathematical Modelling, 36(10), 4714-4727.
  • Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
  • Weber, C.A. ve Ellram, L.M. (1992). Supplier selection using multi-objective programming: a decision support system approach, International Journal of Physical Distribution & Logistics Management, 23(2), 3–14.
  • Weber, C.A., Current, J. ve Desai, A. (2000). An optimization approach to determining the number of vendors to employ, Supply Chain Management: An International Journal, 5(2), 90–98.
  • Wen, L. ve Li, J. (2006). Research of credit grade assessment for suppliers based on multi-layer SVM classiŞer, In Proceedings of the sixth international conference on intelligent systems design and applications, 1, 207–211.
  • Wu, D. (2009). Supplier selection: A hybrid model using DEA, decision tree and neural network, Expert Systems with Applications, 36, 9105–9112.
  • Wu, T. ve Blackhurst, J. (2009). Supplier evaluation and selection: an augmented DEA approach, International Journal of Production Research, 47(16), 4593-4608.

SUPPLIER SELECTION WITH SUPPORT VECTOR REGRESSION AND TWIN SUPPORT VECTOR REGRESSION

Yıl 2016, Cilt: 17 Sayı: 2, 241 - 253, 01.07.2016

Öz

Suppliers’ selection problem has attracted considerable research interest in recent years. Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, we applied the support vector regression SVR and twin support vector regression TSVR techniques to predict the supplier credit index. In practice, the suppliers’ samples are very insufficient. SVR and TSVR are adaptive to deal with small samples. The prediction accuracies for SVR and TSVR methods are compared to choose appropriate suppliers. The actual examples illustrate that TSVR methods are superior to SVR

Kaynakça

  • Abdollahi, M., Arvan, M. ve Razmi, J. (2015). An integrated approach for supplier portfolio selection: Lean or agile?, Expert Systems with Applications, 42(1), 679-690.
  • Arikan, F. ve Kucukce, Y.S. (2012). A supplier selection-evaluation problem for the purchase action and its solution, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(2), 255-264.
  • Bruno, G. ve Esposito, E., Genovese, A., Passaro, R. (2012). AHP-based approaches for supplier evaluation: Problems and perspectives, Journal of Purchasing and Supply Management, 18(3), 159-172.
  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery, 2(2), 121- 167.
  • Cherkassky, V. ve Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17, 113–126.
  • Choi, T.Y. ve Hartley, J.L. (1996). An exploration of supplier selection practices across the supply chain, Journal of Operations Management, 14(4), 333–343.
  • Choy, K.L. ve Lee, W.B. (2002). A generic tool for the selection and management of supplier relationships in an outsourced manufacturing environment: the application of case based reasoning, Logistics Information Management, 15(4), 235–253.
  • Choy, K.L., Lee, W.B. ve Lo, V. (2003). Design of an intelligent supplier relationship management system: A hybrid case based neural network approach, Expert Systems with Applications, 24(2), 225–237.
  • Choy, K.L., Lee, W.B., Lau, H.C.W., Lu, D. ve Lo, V. (2004). Design of an intelligent supplier relationship management system for new product development, International Journal of Computer Integrated Manufacturing, 17(8), 692–715.
  • Dagdeviren, M. ve Eraslan, E. (2008). Supplier selection using promethee sequencing method, Journal of the Faculty of Engineering and Architecture of Gazi University, 23(1), 69-75.
  • Fung, G. ve Mangasarian, O. (2005). Multicategory proximal support vector machine classifiers, Machine Learning, 59, 77–97.
  • Ghorai, S., Mukherjee, A. ve Dutta, P. (2009). Nonparallel plane proximal classifier, Signal Processing, 89(4), 510–522.
  • Gunes, T. ve Polat, E. (2009). Feature selection in facial expression analysis and its effect on multi-svm classifiers, Journal of The Faculty of Engineering and Architecture of Gazi University, 24(1), 7-14.
  • Guo, X., Yuan, Z. ve Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine, Expert Systems with Applications, 36(3), 6978-6985.
  • Guo, X., Zhu, W. ve Shi, J. (2014). Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection, Expert Systems with Applications, 41(4), 2083-2097.
  • Guosheng, H. ve Guohong, Z. (2008). Comparison on neural networks and support vector machines in suppliers' selection, Journal of Systems Engineering and Electronics, 19(2), 316-320.
  • Hsu, C.-W., Chang, C.-C. ve Lin, C.-J. (2004). A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University.
  • Ince, H. ve Trafalis, T.B. (2007). Kernel principal component analysis and support vector machines for stock price prediction, IIE Transactions, 39(6), 629-637.
  • Ince, H. ve Trafalis, T.B. (2008). Short term forecasting with support vector machines and application to stock price prediction, International Journal of General Systems, 37(6), 677- 687.
  • Jain, V., Wadhwa, S. ve Deshmukh, S.G. (2007). Supplier selection using fuzzy association rules mining approach, International Journal of Production Research, 45(6), 1323–1353.
  • Jayadeva, Khemchandani, R. ve Chandra, S. (2007). Twin support vector machines for pattern classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 905–910.
  • Juang, C.-F. ve Shiu, S.-J. (2008). Using self-organizing fuzzy network with support vector learning for face detection in color images, Neurocomputing, 71(16-18), 3409-3420.
  • Kao, C. ve Liu, S.-T. (2000). Fuzzy efficiency measures in data envelopment analysis, Fuzzy Sets and Systems, 113(3), 427–437.
  • Kar, A.K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network, Journal of Computational Science, 6(1), 23-33.
  • Kim, H.-C., Pang, S., Je, H.-M., Kim, D. ve Bang, S.Y. (2003). Constructing support vector machine ensemble, Pattern Recognition, 36(12), 2757-2767.
  • Kumar, M. ve Gopal, M. (2009). Least squares twin support vector machines for pattern classification, Expert Systems with Applications, 36(4), 7535–7543.
  • Kuo, R.J. ve Lin, Y.J. (2012). Supplier selection using analytic network process and data envelopment analysis, International Journal of Production Research, 50(11), 2852-2863.
  • Kuo, R.J., Hong, S.M., Lin, Y. ve Huang, Y.C. (2008). Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF–THEN rules, Neurocomputing, 71(13-15), 2893–2907.
  • Kuo, R.J., Hong, S.Y. ve Huang, Y.C. (2010). Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection, Applied Mathematical Modelling, 34, 3976–3990.
  • Kuo, R.J., Lee, L.Y. ve Hu, T.-L. (2008). An intelligent decision support system for supplier selection, Proceedings of Tenth International Conference on Enterprise Information Systems, June 12–16, Barcelona – Spain, 241–248.
  • Kuo, R.J., Wang, Y.C. ve Tien, F.C. (2010). Integration of artificial neural network and MADA methods for green supplier selection, Journal of Cleaner Production, 18(12), 1161–1170.
  • Lau, H.C.W., Lee, C.K.M., Ho, G.T.S., Pun, K.F. ve Choy, K.L. (2006). A performance benchmarking system to support supplier selection, International Journal of Business Performance Management, 8(2–3), 132–151.
  • Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X. ve Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method, Expert Systems with Applications, 39(1), 424-430.
  • Lu, C.J., Lee, T.S. ve Chiu, C.C. (2009). Financial time series forecasting using independent component analysis and support vector regression, Decision Support Systems, 47(2), 115- 125.
  • Luo, X., Wu, C., Rosenberg, D. ve Barnes, D. (2009). Supplier selection in agile supply chains: An information-processing model and an illustration, Journal of Purchasing & Supply Management, 15, 249–262.
  • Mohanty, R.P. ve Deshmukh, S.G. (1993). Using of analytic hierarchic process for evaluating sources of supply, International Journal of Physical Distribution & Logistics Management, 23(3), 22–28.
  • Narasimhan, R., Talluri, S. ve Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: an empirical examination, Journal of Supply Chain Management, 37(3), 28–37.
  • Noorul Haq, A. ve Kannan, G. (2006). Design of an integrated supplier selection and multi- echelon distribution inventory model in a built-to-order supply chain environment, International Journal of Production Research, 44(10), 1963–1985.
  • Nydick, R.L. ve Hill, R.P. (1992). Using the analytic hierarchy process to structure the supplier selection procedure, International Journal of Purchasing and Materials Management, 28(2), 31–36.
  • Peng, X. (2010). TSVR: an efficient twin support vector machine for regression, Neural Networks, 23(3), 365–372.
  • Sagiroglu, S., Yolacan, E.N. ve Yavanoglu, U. (2011). Designing and developing an intelligent intrusion detection system, Journal of The Faculty of Engineering and Architecture of Gazi University, 26(2), 325-340.
  • Sun, H.-L., Xie, J.-Y. ve Xue, Y.-F. (2005). An SVM-based model for supplier selectionusing fuzzy and pairwise comparison, In Proceedings of 2005 international conference on machine learning and cybernetics, 6, 3629–3633.
  • Talluri, S. (2002). Enhancing supply decisions through the use of efficient marginal costs models, Journal of Supply Chain Management, 38(4), 4–10.
  • Talluri, S. ve Narasimhan, R. (2005). A note on a methodology for supply base optimization, IEEE Transactions on Engineering Management, 52(1), 130–139.
  • Talluri, S. ve Narasimhan, R. (2003). Vendor Evaluation with performance variability: a max- min approach, European Journal of Operational Research, 146(3), 543–552.
  • Talluri, S., Narasimhan, R. ve Nair, A. (2006). Vendor performance with supply risk: a chance- constrained DEA Approach, International Journal of Production Research, 100(2), 212– 222.
  • Tseng, M.-L., Chiang, J.H. ve Lan, L.W. (2009). Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral, Computers & Industrial Engineering, 57(1), 330-340.
  • Vahdani, B., Iranmanesh, S.H., Meysam Mousavi, S. ve Abdollahzade, M. (2012). A locally linear neuro-fuzzy model for supplier selection in cosmetics industry, Applied Mathematical Modelling, 36(10), 4714-4727.
  • Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
  • Weber, C.A. ve Ellram, L.M. (1992). Supplier selection using multi-objective programming: a decision support system approach, International Journal of Physical Distribution & Logistics Management, 23(2), 3–14.
  • Weber, C.A., Current, J. ve Desai, A. (2000). An optimization approach to determining the number of vendors to employ, Supply Chain Management: An International Journal, 5(2), 90–98.
  • Wen, L. ve Li, J. (2006). Research of credit grade assessment for suppliers based on multi-layer SVM classiŞer, In Proceedings of the sixth international conference on intelligent systems design and applications, 1, 207–211.
  • Wu, D. (2009). Supplier selection: A hybrid model using DEA, decision tree and neural network, Expert Systems with Applications, 36, 9105–9112.
  • Wu, T. ve Blackhurst, J. (2009). Supplier evaluation and selection: an augmented DEA approach, International Journal of Production Research, 47(16), 4593-4608.
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Hüseyin İnce Bu kişi benim

Salih Zeki İmamoğlu Bu kişi benim

Yayımlanma Tarihi 1 Temmuz 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 17 Sayı: 2

Kaynak Göster

APA İnce, H., & İmamoğlu, S. Z. (2016). DESTEK VEKTÖR REGRESYON VE İKİZ DESTEK VEKTÖR REGRESYON YÖNTEMİ İLE TEDARİKÇİ SEÇİMİ. Doğuş Üniversitesi Dergisi, 17(2), 241-253.