Dynamical analysis and decision support system of production management

¹ Division of Logistics, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea
² Division of Mechanical Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea
³ Northeast-Asia Shipping and Port Logistics Research Center, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea

^* Corresponding author: ssyou@kmou.ac.kr

Received: 23 September 2022
Accepted: 12 August 2023

Abstract

Nonlinear system dynamics and feedback control theory are presented for management optimization of supply chain systems. Linearization and simplification methods are widely used in analyzing the system dynamics of supply chains because actual production models are highly complex and nonlinear systems. With advanced system dynamics, it is possible to deal directly with nonlinear dynamical problems without linear approximate methods so that the decision-makers can obtain more accurate results for systematic management strategies. This paper proposes a nonlinear system theory to explore dynamical behavior and control synthesis of production-distribution systems using 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 the 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 a superb decision scheme powered by a control algorithm, novel supply chain software can learn an ever-fluctuating production flow and anticipate the need for changes in a real market.