Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems
- https://doi.org/10.1080/18756891.2016.1175816How to use a DOI?
- Standard ready-mixed concrete, Multi-response optimization, Taguchi method, Fuzzy TOPSIS, Artificial neural networks
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.
- © 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
TY - JOUR AU - Barış Şimşek AU - Yusuf Tansel İç AU - Emir Hüseyin Şimşek PY - 2016 DA - 2016/06 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 - https://doi.org/10.1080/18756891.2016.1175816 ID - Şimşek2016 ER -