Dynamic Analysis and Optimization of Chaotic Supply Chain

Abstract

Today's global markets are increasingly dynamic and volatile. This dynamic and volatile nature creates various kinds of uncertainties in the supply chain, the most important of which are demand uncertainty, transportation uncertainty, and forecast uncertainty. The supply chain has long been recognized as a linear system, with raw materials entering from upstream and finished goods exiting downstream. Traditionally, each entity in this supply chain has been able to remain resilient to both internal and external volatility by working independently of each other and holding large stock. Supply chain management involves many interrelated and coupled processes, many of which are sensitive to the effects of the uncertainties. In order to identify and eliminate these uncertainties, this dissertation proposed solutions that help to effectively manage the supply chain network at different stages of the decision-making process from systems engineering and control theory. Moreover, many research challenges that need to be addressed in the application of interdisciplinary theories are presented. This dissertation presents a multi-echelon supply chain system with parameter perturbations and external disturbances to exhibit chaotic nonlinear behaviors. A small change in the input variables in the supply chain system can lead to the entirely different predicted outputs due to the nature of chaotic behavior. Furthermore, diverse uncertainties and external disturbances make the supply chain management more complex and difficult. To address these issues, the adaptive super-twisting sliding mode control (ASTW-SMC) and adaptive fractional order sliding mode control (AFOSMC) algorithms are applied to manage chaotic supply chain system. Particularly, both of the advanced SMC algorithms have been designed for synchronization of the supply chain system. Next, the robust control algorithm with adaptive law for the closed-loop system has been proved by using Lyapunov stability theorem. Then, extensive numerical simulations are conducted to demonstrate the validity of the active control synthesis for optimal operations management of chaotic supply chain networks. The control algorithm based on system theory provides satisfactory performance on achieving synchronization of the chaotic supply system. The control system theory can be expanded into new integration software applications for operations management of supply chain networks. Finally, the presented control synthesis with dynamical analysis is essential for strategic decision-makers in the modern supply chain management.