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Implementation of Fuzzy and Adaptive Neuro-Fuzzy Inference Systems in Optimization of Production Inventory Problem |
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PP: 289-298 |
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doi:10.18576/amis/110135
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Author(s) |
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Ahmed Abdel-Aleem,
Mahmoud A. El-Sharief,
Mohsen A. Hassan,
Mohamed G. El-Sebaie,
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Abstract |
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| Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is
reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine
failure occur and the production does not resume before repair. In this paper we will study and analyze the optimal lot size in a
real production system which is not completely reliable. To obtain the optimal production quantity. Fuzzy Inference System (FIS) and
Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling and simulation. This approach combines the advantages
of rule-base fuzzy system and the learning capability benefit of neural networks. In the case study of cement industry, ANFIS prediction
has shown very good agreement with the real production quantity. This model can be extended for any inventory production quantity
problems if the industrial data are available. |
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