Decision support system for managing multi-echelon supply chain networks against disruptions using adaptive fractional order control algorithm

Abstract

Dynamical analysis and management optimization of supply chain system are explored by utilizing four-stage hyperchaotic Lorenz-Stenflo equation. The supply chain risks are represented by parametric variations and disturbance against disruptions. Nonlinear behaviors are intensely investigated by eigenvalue and bifurcation analysis to identify supply chain risks. Then phase portraits are presented to illustrate the bullwhip effect influencing various stages of multi-echelon supply chains. Along with dynamic identification, resilient supply chains have been developed by realizing an adaptive fractional-order controller. By employing control theory on managerial applications, efficient algorithm can be implemented for optimization problems while reducing potential volatility. Performance criteria have been exploited to validate the control methodology. Based on management algorithms, decision-makers cope with chaos suppression and synchronization problems effectively, ensuring sustainability and reliability. By utilizing control theory, the decision-making strategy can offer new insights into how to effectively manage digital supply chain networks against market volatility.