International Journal of Computational Intelligence Systems

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Volume 9, Issue 3, June 2016, Pages 525 - 543

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Authors
Barış Şimşek1, barissimsek@karatekin.edu.tr, Yusuf Tansel İç2, ytansel@baskent.edu.tr, Emir Hüseyin Şimşek3, simsek@eng.ankara.edu.tr
1Department of Chemical Engineering, Faculty of Engineering, Çankırı Karatekin University, 18200, Uluyazı Kampüsü, Merkez, Çankırı, Turkey
2Department of Industrial Engineering, Faculty of Engineering, Baskent University, 06810 Baglica, Etimesgut, Ankara, Turkey
3Department of Chemical Engineering, Faculty of Engineering, Ankara University, 06100, Tandoğan, Ankara, Turkey
Corresponding Author’s
Received 12 June 2015, Accepted 1 March 2016, Available Online 1 June 2016.
DOI
10.1080/18756891.2016.1175816How to use a DOI?
Keywords
Standard ready-mixed concrete; Multi-response optimization; Taguchi method; Fuzzy TOPSIS; Artificial neural networks
Abstract

In this study, a fuzzy multi-response standard ready-mixed concrete (SRMC) optimization problem is addressed. This problem includes two conflicting quality optimization objectives. One of these objectives is to minimize the production cost. The other objective is to assign the optimal parameter set of SRMC’s ingredient to each activity. To solve this problem, a hybrid fuzzy multi-response optimization and artificial neural network (ANN) algorithm is developed. The ANN algorithm is integrated into the multi-response SRMC optimization framework to predict and improve the quality of SRMC. The results show that fuzzy multi-response optimization model is more effective than crisp multi-response optimization model according to final production cost. However, the ANN model also gave more accurate results than the fuzzy model considering the regression analysis results.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 3
Pages
525 - 543
Publication Date
2016/06/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1175816How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Barış Şimşek
AU  - Yusuf Tansel İç
AU  - Emir Hüseyin Şimşek
PY  - 2016
DA  - 2016/06/01
TI  - Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems
JO  - International Journal of Computational Intelligence Systems
SP  - 525
EP  - 543
VL  - 9
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1175816
DO  - 10.1080/18756891.2016.1175816
ID  - Şimşek2016
ER  -