Chaos synchronization for a class of uncertain chaotic supply chain and its control by ANFIS




supply chain, chaotic, synchronization, ANFIS


In this paper, modelling of a three-level chaotic supply chain network. This model has the uncertainty of the retailer in the manufacturer. An adaptive neural fuzzy method has been proposed to synchronize the two chaotic supply chain networks. To train adaptive neural fuzzy controller, first, a nonlinear feedback control method is designed. Then, using Lyapanov theory, it is proved that the nonlinear feedback controller can reduce the synchronization error to zero in a finite time. The simulation results show that the proposed neural fuzzy controller architecture well controls the synchronization of the two chaotic supply chain networks. In the other part of the simulation, a comparison is made between the performance of the nonlinear controller and the adaptive neural fuzzy. Also, in the simulation results, the controller signal is depicted. This signal indicates that the cost of implementation in the real world is not high and is easily implemented.


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Author Biographies

S.M. Hamidzadeh, University of Science and Technology of Mazandaran

Department of Industrial Engineering

Mohsen Rezaei, University of Science and Technology of Mazandaran

Department of Industrial Engineering

Mehdi Ranjbar-Bourani, University of Science and Technology of Mazandaran

Department of Industrial Engineering


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How to Cite

Hamidzadeh, S. M., Rezaei, M., & Ranjbar-Bourani, M. (2023). Chaos synchronization for a class of uncertain chaotic supply chain and its control by ANFIS. International Journal of Production Management and Engineering, 11(2), 113–126.