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Optimisation of net profit with uncertain inputs in manufacturing environments by integration of neural networks, genetic algorithm and fuzzy regression

Author

Listed:
  • Ali Azadeh
  • Ali Eydi
  • Zeinab Raoofi
  • Hamed Rafiei

Abstract

This paper presents an integrated artificial fuzzy regression, neural network (ANN) and genetic algorithm (GA) for optimisation of profit with uncertain inputs. Generally, truncations of α has been used to study the fuzzy regression model. In this paper, fuzzy regression is accomplished by the fuzzy neural networks and the necessary neural nets training is proposed by the fuzzy numbers which is based on genetic algorithm. The proposed neural net learning method based on GA is claimed to be a better substitute because of its higher efficiency. To show the applicability and superiority of the proposed approach an actual case study (manufacturer of aluminium heater) is presented, applied and discussed for the improved fuzzy regression by the integrated neural network and genetic algorithm. This is the first study that integrates fuzzy regression, GA and ANN for optimisation of net profit in an uncertain manufacturing environment.

Suggested Citation

  • Ali Azadeh & Ali Eydi & Zeinab Raoofi & Hamed Rafiei, 2014. "Optimisation of net profit with uncertain inputs in manufacturing environments by integration of neural networks, genetic algorithm and fuzzy regression," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 16(1), pages 88-101.
    • Handle: RePEc:ids:ijisen:v:16:y:2014:i:1:p:88-101
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