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Fuzzy Modeling for Uncertainty Analysis of a Door-Joinery Process

Year 2018, Volume: 20 Issue: 3, 565 - 575, 15.12.2018

Abstract

Organizations have been working on the
improvement studies focused on customer requirements in their management,
production/service processes to keep up with global competition area. The most
effective examples in Turkey can be seen in construction sector. The famous
Turkish organizations in global construction industry have been working on
improving their resource managements and taking them under control.
Organizations set up their ancillary industries to fulfill their own
requirements. Besides, there is an increased interest in scientific techniques
for improving processes. In this study, we proposed an approach to model a
production process of a door-joinery factory, which is set up to satisfy the Turkish
corporation group’s requirements. This approach has three phases: i)
Determining problem-resources for a door production and creating databases, ii)
Calculating membership values for the databases, iii) Modeling the databases
with their memberships using fuzzy (regression) functions. Factory’s problem is
related to a door production-time, which takes long than the factory’s target. In
the first phase, the effective causes of the problem are investigated using
fishbone diagram. After determining the effective main parts and their operations
by the production-time via pareto analysis, the databases are created. In the
second, membership values of the databases are calculated to identify
data-based uncertainties. In the third phase, databases and their memberships
are modeled by fuzzy (regression) functions. According to the proposed
approach, fuzzy clustering structures of the door leaf and frame databases are
occurred with 93.0% and 94.0% accuracy rates, respectively. Consequently, fuzzy
functions for a door production-time give better performance-results (door-leaf: R2=73.8%, RMSE=0.455;
door-frame: R2=72.8%, RMSE=0.553) than convenient
models.

References

  • Aladag, C.H., Turksen, I.B., Dalar, A.Z., Egrioglu, E., Yolcu, U. (2014). Application of Type-1 fuzzy functions approach for time series forecasting. Turkish Journal of Fuzzy Systems, 5(1): 1-9.
  • Bardak, S., Tiryaki, S., Bardak, T., Aydın, A. (2016). Predictive performance of artificial neural network and multiple linear regression models in predicting adhesive bonding strength of wood. Strength of Materials, 48(6): 811-824.
  • Bardak, T., Bardak, S. (2017). Prediction of wood density by using red-green-blue (rgb) color and fuzzy logic techniques. Journal of Polytechnic, 20(4): 979-984.
  • Başkır, M.B. (2006). Kapı-doğrama sürecinde altı sigma yaklaşımı. 5. İstatistik Günleri Sempozyumu Bildiriler Kitabı, s. 71-78, Antalya.
  • Başkır, M.B., Türkşen, I.B. (2010). An uncertainty analysis of supplier selection by fuzzy logic. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010), Barcelona, Spain.
  • Başkır, M.B. (2016). Type-1 Fuzzy Modeling for DuPont Analysis on Turkish Insurance Sector. Turkish Journal of Fuzzy Systems, 7(1): 29-40.
  • Bezdek, J.C. (1974a). Numerical taxonomy with fuzzy sets. Journal of Mathematical Biology, 1: 57-71.
  • Bezdek, J.C. (1974b). Cluster validity with fuzzy sets. Journal of Cybernetics, 3(3): 58-73.
  • Bezdek, J.C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press: New York.
  • Breyfogle F.W. (2003). III, Implementing six sigma: smarter solutions using statistical methods. John Wiley & Sons, Hoboken, N.J.
  • Çelikyılmaz, A., Türkşen, I.B. (2008a). Validation criteria for enhanced fuzzy clustering. Pattern Recognition Letters, 29(2): 97-108.
  • Demirci, M. (1999). Fuzzy functions and their fundamental properties. Fuzzy Sets and Systems, 106(2): 239–246.
  • Demirci, M. (2003). Foundations of fuzzy functions and vague algebra based on many valued equivalence relations, part I: fuzzy functions and their applications. International Journal of General Systems, 32(2): 123–155.
  • Fukuyama, Y., Sugeno, M. (1989). A new method of choosing the number of clusters for the fuzzy c-means method, in Proceedings of 5th Fuzzy Systems Symposium, pp. 247-250.
  • Ishikawa, K. (1990) Introduction to Quality Control, Taylor & Francis.
  • Kim, M., Ramakrishna, R.S. (2005). New indices for cluster validity assessment. Pattern Recognition Letters, 26(15): 2353-2363.
  • Tang, Y., Sun, F., Sun, Z. (2005). Improved validation index for fuzzy clustering. American Control Conference, 1120-1125, USA.
  • Taormina, R., Chau K-W., Sivakumar, B. (2015). Neural network river forecasting through baseflow separation and binary-coded swarm optimization. Journal of Hydrology, 529(3): 1788-1797.
  • Türkşen, I.B. (2008). Fuzzy function with LSE. Applied Soft Computing, 8(3): 1178-1188.
  • Türkşen, I.B. (2012). A review of developments from fuzzy rule bases to fuzzy functions. Hacettepe Journal of Mathematics and Statistics, 41(3): 347-359.
  • Wang, W-C., Xu, D-M., Chau, K-W., Lei, G-J. (2014). Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resources Management, 28(12): 4183-4200.
  • Xie, X.L., Beni, G.A. (1991). Validity measure for fuzzy clustering. IEEE Trans. Pattern and Machine Intelligence, 3(8): 841-846. Zadeh, L.A. (1965). Fuzzy sets. Information Control, 8(3): 338-353.
  • Zarandi, MHF., Zarinbal, M., Ghanbari, N., Turksen, IB. (2013). A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction. Information Sciences, 222(10): 213-228.

Bir Kapı-Doğrama Süreci Belirsizlik Analizi için Bulanık Modelleme

Year 2018, Volume: 20 Issue: 3, 565 - 575, 15.12.2018

Abstract

İşletmeler günümüz
rekabet ortamına ayak uydurabilmek için yönetim, üretim/hizmet süreçlerinin
bütününde, müşteri memnuniyetinin ön planda tutulduğu iyileştirme çalışmalarında
bulunmaktadır. Türkiye’de bu çalışmaların en etkili örnekleri inşaat sektöründe
gerçekleştirilmektedir. Küresel inşaat sanayinde kendini ispatlamış birçok Türk
firması mevcut kaynak yönetimini iyileştirme ve kontrol altına alma çalışmaları
içerisindedir. İşletmeler bünyelerinde yan sanayi gereksinimlerini
karşıladıkları birimler kurmaktadırlar. Bunun yanı sıra, işletmeler için süreç
iyileştirme çalışmalarında bilimsel yöntemlere dayalı değerlendirmelerin önemi
artmaktadır. Bu çalışmada, bir şirketler topluluğunun yan sanayi ihtiyaçlarını
karşılamak amacı ile kurduğu kapı doğrama fabrikasında birim kapı imalat sürecinin
modellenmesi için bir yaklaşım önerilmektedir. Bu yaklaşım üç aşamadan
oluşmaktadır: i) Birim kapı imalatında problem kaynağının tespit edilerek veri
tabanlarının oluşturulması, ii) Oluşturulan veri tabanları için üyelik
değerlerinin hesaplanması, iii) Veri tabanlarının üyelikleri ile birlikte
bulanık (regresyon) fonksiyonlar kullanılarak modellenmesi. Fabrikada problem
birim kapı imalatının hedeflenenden daha uzun sürmesidir. Birinci aşamada,
balıkkılçığı diyagramı ile problemin etkili nedenleri belirlenmektedir. Pareto
analizi ile kapı imalatında harcanan süre bakımından etkili olan ana parçalar
ve alt işlemleri seçilerek veri tabanları oluşturulmaktadır. İkinci aşamada, bu
veri tabanlarının yapısı kaynaklı belirsizlikler için bulanık öbekleme ile
üyelik değerleri hesaplanmaktadır. Üçüncü aşamada, veri tabanları ve üyelikleri
bulanık (regresyon) fonksiyonları ile modellenmektedir. Önerilen yaklaşıma göre
belirlenen kapı kanadı ve kasası veri tabanlarının bulanık öbeklenme yapıları,
sırasıyla, %93,0 ve %94,0 doğruluk oranları ile oluşturulmuştur. Sonuç olarak, birim
kapı imalat-süreleri için belirlenen bulanık fonksiyon performans sonuçlarının
(kapı kanadı için R2=%73,8
ve HKOK=0,455; kapı kasası için R2=%72,8 ve HKOK=0,553) klasik regresyondan daha iyi
olduğu görülmüştür.

References

  • Aladag, C.H., Turksen, I.B., Dalar, A.Z., Egrioglu, E., Yolcu, U. (2014). Application of Type-1 fuzzy functions approach for time series forecasting. Turkish Journal of Fuzzy Systems, 5(1): 1-9.
  • Bardak, S., Tiryaki, S., Bardak, T., Aydın, A. (2016). Predictive performance of artificial neural network and multiple linear regression models in predicting adhesive bonding strength of wood. Strength of Materials, 48(6): 811-824.
  • Bardak, T., Bardak, S. (2017). Prediction of wood density by using red-green-blue (rgb) color and fuzzy logic techniques. Journal of Polytechnic, 20(4): 979-984.
  • Başkır, M.B. (2006). Kapı-doğrama sürecinde altı sigma yaklaşımı. 5. İstatistik Günleri Sempozyumu Bildiriler Kitabı, s. 71-78, Antalya.
  • Başkır, M.B., Türkşen, I.B. (2010). An uncertainty analysis of supplier selection by fuzzy logic. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010), Barcelona, Spain.
  • Başkır, M.B. (2016). Type-1 Fuzzy Modeling for DuPont Analysis on Turkish Insurance Sector. Turkish Journal of Fuzzy Systems, 7(1): 29-40.
  • Bezdek, J.C. (1974a). Numerical taxonomy with fuzzy sets. Journal of Mathematical Biology, 1: 57-71.
  • Bezdek, J.C. (1974b). Cluster validity with fuzzy sets. Journal of Cybernetics, 3(3): 58-73.
  • Bezdek, J.C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press: New York.
  • Breyfogle F.W. (2003). III, Implementing six sigma: smarter solutions using statistical methods. John Wiley & Sons, Hoboken, N.J.
  • Çelikyılmaz, A., Türkşen, I.B. (2008a). Validation criteria for enhanced fuzzy clustering. Pattern Recognition Letters, 29(2): 97-108.
  • Demirci, M. (1999). Fuzzy functions and their fundamental properties. Fuzzy Sets and Systems, 106(2): 239–246.
  • Demirci, M. (2003). Foundations of fuzzy functions and vague algebra based on many valued equivalence relations, part I: fuzzy functions and their applications. International Journal of General Systems, 32(2): 123–155.
  • Fukuyama, Y., Sugeno, M. (1989). A new method of choosing the number of clusters for the fuzzy c-means method, in Proceedings of 5th Fuzzy Systems Symposium, pp. 247-250.
  • Ishikawa, K. (1990) Introduction to Quality Control, Taylor & Francis.
  • Kim, M., Ramakrishna, R.S. (2005). New indices for cluster validity assessment. Pattern Recognition Letters, 26(15): 2353-2363.
  • Tang, Y., Sun, F., Sun, Z. (2005). Improved validation index for fuzzy clustering. American Control Conference, 1120-1125, USA.
  • Taormina, R., Chau K-W., Sivakumar, B. (2015). Neural network river forecasting through baseflow separation and binary-coded swarm optimization. Journal of Hydrology, 529(3): 1788-1797.
  • Türkşen, I.B. (2008). Fuzzy function with LSE. Applied Soft Computing, 8(3): 1178-1188.
  • Türkşen, I.B. (2012). A review of developments from fuzzy rule bases to fuzzy functions. Hacettepe Journal of Mathematics and Statistics, 41(3): 347-359.
  • Wang, W-C., Xu, D-M., Chau, K-W., Lei, G-J. (2014). Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resources Management, 28(12): 4183-4200.
  • Xie, X.L., Beni, G.A. (1991). Validity measure for fuzzy clustering. IEEE Trans. Pattern and Machine Intelligence, 3(8): 841-846. Zadeh, L.A. (1965). Fuzzy sets. Information Control, 8(3): 338-353.
  • Zarandi, MHF., Zarinbal, M., Ghanbari, N., Turksen, IB. (2013). A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction. Information Sciences, 222(10): 213-228.
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Wood Machinary, Occupational Safety and Health, Business Administration
Authors

Mükerrem Bahar Başkır

Selman Karayılmazlar

Publication Date December 15, 2018
Published in Issue Year 2018 Volume: 20 Issue: 3

Cite

APA Başkır, M. B., & Karayılmazlar, S. (2018). Bir Kapı-Doğrama Süreci Belirsizlik Analizi için Bulanık Modelleme. Bartın Orman Fakültesi Dergisi, 20(3), 565-575.


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