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Journals / Doğuş Üniversitesi Dergisi / 2016 / Cilt: 17 - Sayı: 2

SUPPLIER SELECTION WITH SUPPORT VECTOR REGRESSION AND TWIN SUPPORT VECTOR REGRESSION

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

HÜSEYİN İNCE
SALİH ZEKİ İMAMOĞLU
Pages
241–253
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The actual examples illustrate that TSVR methods are superior to SVR

📌 3 Key Takeaways

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Objective & Hypothesis:Suppliers’ selection problem has attracted considerable research interest in recent years.
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Key Findings & Data:Recent literature show that artificial intelligence techniques achieve better performance than traditional statistical methods.
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Conclusion & Impact:The actual examples illustrate that TSVR methods are superior to SVR
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Tedarikçi Seçimi · Destek Vektör Regresyon · İkiz Destek Vektör Regresyon

Abstract

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

Özet

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.

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Cite This Article

İNCE, H., İMAMOĞLU, S. Z. (2016). SUPPLIER SELECTION WITH SUPPORT VECTOR REGRESSION AND TWIN SUPPORT VECTOR REGRESSION. Doğuş Üniversitesi Dergisi, Cilt: 17 - Sayı: 2, 241–253. https://ajindex.com/articles/125971

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References (14)

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Fung, G. ve Mangasarian, O. (2005). Multicategory proximal support vector machine classifiers, Machine Learning, 59, 77–97.
  11. Ghorai, S., Mukherjee, A. ve Dutta, P. (2009). Nonparallel plane proximal classifier, Signal Processing, 89(4), 510–522.
  12. 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.
  13. 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.
  14. Guo, X., Zhu, W. v