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Adaptif Sinir Ağına Dayalı Bulanık Çıkarım Sistemi İle Dönen Ürünlerin Fiyatlandırılması

Year 2017, Volume 5, Issue 2, 2017, 207 - 214, 15.10.2017
https://doi.org/10.17093/alphanumeric.309051

Abstract

Tersine lojistik faaliyetleri firmalar için hem çevreci hem de ekonomik bir yaklaşım olarak son yıllarda giderek artan bir öneme sahip olmaktadır. Firmalar, dönen ürünleri toplayarak çeşitli işlemler sonrasında geri kazanım gerçekleştirmektedir. Dönen ürünlerin toplanmasında, her bir dönen ürünün fonksiyonel durumu birbirinden farklı olduğu için kullanılmış ürünü geri alma bedeline karar vermek önemli bir sorundur. Bu nedenle, bu çalışmada dönen ürünlerin toplanmasında kullanılabilecek bir fiyatlandırma yaklaşımı önerilmiştir. Farklı ürün modelleri olabileceğinden ve dönen ürün fiyatının yeni ürün fiyatından etkilenebileceğinden dolayı dönen ürünün fiyatı, yeni ürün fiyatına oranı olarak tahmin edilmiştir. Çalışmada dönen ürünün yeni ürün fiyatına oranı adaptif sinir ağına dayalı bulanık çıkarım sistemi ile modellenmiş ve kullanılmış cep telefonlarının toplanmasında uygulanmıştır. Dört farklı çıkış yılına sahip telefon modelinin genel görünüm ve fonksiyonellik parametreleri göz önüne alınmıştır. Sonuçlar incelendiğinde önerilen yaklaşımın uzman görüşüne oldukça yakın sonuçlar verdiği görülmüştür.

References

  • TCŞB, Atık Elektrikli Ve Elektronik Eşyaların Kontrolü Yönetmeliği, T.C.Ç.v.Ş. Bakanlığı, Editor. 2012, Resmi Gazete.
  • Blumberg, D.F., Introduction to management of reverse logistics and closed loop supply chain processes. 2004: CRC Press.
  • Pochampally, K.K., S. Nukala, and S.M. Gupta, Strategic planning models for reverse and closed-loop supply chains. 2008: CRC Press.
  • Thierry, M., et al., Strategic issues in product recovery management. California management review, 1995. 37(2): p. 114-135.
  • Guide Jr, V.D.R., et al., Supply-chain management for recoverable manufacturing systems. Interfaces, 2000. 30(3): p. 125-142.
  • Qiaolun, G., J. Jianhua, and G. Tiegang, Pricing management for a closed-loop supply chain. Journal of revenue and pricing management, 2008. 7(1): p. 45-60.
  • Liang, Y., S. Pokharel, and G.H. Lim, Pricing used products for remanufacturing. European Journal of Operational Research, 2009. 193(2): p. 390-395.
  • Xiong, Y., et al., Dynamic pricing models for used products in remanufacturing with lost-sales and uncertain quality. International journal of production economics, 2014. 147: p. 678-688.
  • Seidi, M. and A.M. Kimiagari, A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES. South African Journal of Industrial Engineering, 2010. 21(2): p. 131-148.
  • Pokharel, S. and Y.J. Liang, A model to evaluate acquisition price and quantity of used products for remanufacturing. International Journal of Production Economics, 2012. 138(1): p. 170-176.
  • Guide Jr, V.D.R., R.H. Teunter, and L.N. Van Wassenhove, Matching demand and supply to maximize profits from remanufacturing. Manufacturing & Service Operations Management, 2003. 5(4): p. 303-316.
  • Franke, C., et al., Remanufacturing of mobile phones—capacity, program and facility adaptation planning. Omega, 2006. 34(6): p. 562-570.
  • Robotis, A., S. Bhattacharya, and L.N. Van Wassenhove, The effect of remanufacturing on procurement decisions for resellers in secondary markets. European Journal of Operational Research, 2005. 163(3): p. 688-705.
  • Zadeh, L.A., Fuzzy sets. Information and Control, 1965. 8(3): p. 338-353.
  • Buragohain, M. and C. Mahanta, A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 2008. 8(1): p. 609-625.
  • Czogala, E. and J. Leski, Fuzzy and neuro-fuzzy intelligent systems. Vol. 47. 2012: Physica.
  • Jang, J.-S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993. 23(3): p. 665-685.

Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System

Year 2017, Volume 5, Issue 2, 2017, 207 - 214, 15.10.2017
https://doi.org/10.17093/alphanumeric.309051

Abstract

In recent years, reverse logistics have become increasingly important for the firms as a both environmental and economical approach. By collecting the returned products, firms realize to recover after kind of activities. In return products collection, due to the fact that each returned products have different functionality, determining the acquisition price of the used products is an important problem. For this reason, a pricing approach that can be used for collecting returned products is proposed in this study. Since the different product models can be exist and the acquisition price can be affected by the new product price, the acquisition price is predicted by the ratio of the new product price to acquisition price. In this study, the acquisition price ratio to new product price is modeled by the adaptive neuro fuzzy inference system and a case study is conducted for the used cell phones collection. Four phone models that have different release dates take into consideration with general appearance and functionality parameters. When the results are examined, the proposed method prediction’s is pretty close to the expert view.

References

  • TCŞB, Atık Elektrikli Ve Elektronik Eşyaların Kontrolü Yönetmeliği, T.C.Ç.v.Ş. Bakanlığı, Editor. 2012, Resmi Gazete.
  • Blumberg, D.F., Introduction to management of reverse logistics and closed loop supply chain processes. 2004: CRC Press.
  • Pochampally, K.K., S. Nukala, and S.M. Gupta, Strategic planning models for reverse and closed-loop supply chains. 2008: CRC Press.
  • Thierry, M., et al., Strategic issues in product recovery management. California management review, 1995. 37(2): p. 114-135.
  • Guide Jr, V.D.R., et al., Supply-chain management for recoverable manufacturing systems. Interfaces, 2000. 30(3): p. 125-142.
  • Qiaolun, G., J. Jianhua, and G. Tiegang, Pricing management for a closed-loop supply chain. Journal of revenue and pricing management, 2008. 7(1): p. 45-60.
  • Liang, Y., S. Pokharel, and G.H. Lim, Pricing used products for remanufacturing. European Journal of Operational Research, 2009. 193(2): p. 390-395.
  • Xiong, Y., et al., Dynamic pricing models for used products in remanufacturing with lost-sales and uncertain quality. International journal of production economics, 2014. 147: p. 678-688.
  • Seidi, M. and A.M. Kimiagari, A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES. South African Journal of Industrial Engineering, 2010. 21(2): p. 131-148.
  • Pokharel, S. and Y.J. Liang, A model to evaluate acquisition price and quantity of used products for remanufacturing. International Journal of Production Economics, 2012. 138(1): p. 170-176.
  • Guide Jr, V.D.R., R.H. Teunter, and L.N. Van Wassenhove, Matching demand and supply to maximize profits from remanufacturing. Manufacturing & Service Operations Management, 2003. 5(4): p. 303-316.
  • Franke, C., et al., Remanufacturing of mobile phones—capacity, program and facility adaptation planning. Omega, 2006. 34(6): p. 562-570.
  • Robotis, A., S. Bhattacharya, and L.N. Van Wassenhove, The effect of remanufacturing on procurement decisions for resellers in secondary markets. European Journal of Operational Research, 2005. 163(3): p. 688-705.
  • Zadeh, L.A., Fuzzy sets. Information and Control, 1965. 8(3): p. 338-353.
  • Buragohain, M. and C. Mahanta, A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 2008. 8(1): p. 609-625.
  • Czogala, E. and J. Leski, Fuzzy and neuro-fuzzy intelligent systems. Vol. 47. 2012: Physica.
  • Jang, J.-S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993. 23(3): p. 665-685.
There are 17 citations in total.

Details

Journal Section Articles
Authors

Yusuf Kuvvetli

Publication Date October 15, 2017
Submission Date April 25, 2017
Published in Issue Year 2017 Volume 5, Issue 2, 2017

Cite

APA Kuvvetli, Y. (2017). Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System. Alphanumeric Journal, 5(2), 207-214. https://doi.org/10.17093/alphanumeric.309051

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