标题：A comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs
期刊名称：International Journal of Engineering Business Management
印刷版ISSN：1847-9790
电子版ISSN：1847-9790
出版年度：2018
卷号：10
DOI：10.1177/1847979018768421
语种：English
出版社：InTech
摘要：Most deterministic optimization models use average values of nondeterministic variables as their inputs. It is, therefore, expected that a model that can accept the distribution of a random variable, while this may involve some more computational complexity, would likely produce better results than the model using the average value. Artificial neural network (ANN) is a standard technique for solving complex stochastic problems. In this research, ANN and adaptive neuro-fuzzy inference system (ANFIS) have been implemented for modeling and optimizing product distribution in a multi-echelon transshipment system. Two inputs parameters, product demand and unit cost of shipment, are considered nondeterministic in this problem. The solutions of ANFIS and ANN were compared to that of the classical transshipment model. The optimal total cost of distribution using the classical model within the period of investigation was 6,332,304.00. In the search for a better solution, an ANN model was trained, tested, and validated. This approach reduced the cost to 4,170,500.00. ANFIS approach reduced the cost to 4,053,661. This implies that 34% of the current operational cost was saved using the ANN model, while 36% was saved using the ANFIS model. This suggests that the result obtained from the ANFIS model also seems marginally better than that of the ANN. Also, the ANFIS model is capable of adjusting the values of input and output variables and parameters to obtain a more robust solution.
