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

Autor: Barış Şimşek, Yusuf Tansel İç, Emir Hüseyin Şimşek
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Zdroj: International Journal of Computational Intelligence Systems, Vol 9, Iss 3 (2016)
Druh dokumentu: article
ISSN: 18756891
1875-6883
DOI: 10.1080/18756891.2016.1175816
Popis: 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.
Databáze: Directory of Open Access Journals
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