Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems
Autor: | Yusuf Tansel İç, Emir H. Şimşek, Barış Şimşek |
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Jazyk: | angličtina |
Rok vydání: | 2016 |
Předmět: |
Mathematical optimization
Optimization problem General Computer Science Neuro-fuzzy Computer science 0211 other engineering and technologies 02 engineering and technology Fuzzy logic lcsh:QA75.5-76.95 Standard ready-mixed concrete Set (abstract data type) Taguchi methods 021105 building & construction 0202 electrical engineering electronic engineering information engineering Multi-response optimization Artificial neural network Artificial neural networks business.industry Regression analysis QA75.5-76.95 Computational Mathematics Taguchi optimization Fuzzy TOPSIS Electronic computers. Computer science Taguchi method 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electronic computers. Computer science business Algorithm |
Zdroj: | International Journal of Computational Intelligence Systems, Vol 9, Iss 3 (2016) |
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: | OpenAIRE |
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