Dynamical analysis and decision support system of production management

Abstract

Nonlinear system dynamics and feedback control theory are presented for management optimization of supply chain system. Linearization and simplification method are widely used in analyzing system dynamics of supply chains because actual production models are highly complex and nonlinear systems. With the advanced system dynamics, it is possible directly to deal with nonlinear dynamical problems without linear approximate methods so that the decision makers can obtain more accurate results for systematic management strategies. This paper aims to propose nonlinear system theory to explore dynamical behavior and control synthesis of production-distribution system by utilizing Forrester’s model. A novel super twisting sliding mode control (SWT-SMC) algorithm has been presented based on adaptation law, ensuring management optimization against disruptions. The closed-loop system stability has been guaranteed by using Lyapunov theory. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the adaptive super twisting sliding mode control (ASWT-SMC) algorithm. Four types of decision criteria have been employed to compare system performance between control strategies. With superb decision scheme powered by control algorithm, novel supply chain software can learn an ever-fluctuating production flow and anticipate the need for changes in real market.