Seaport Throughput Analysis and Global Risk Management by Using Effective Decision‐Making Theory: A Case Study of Vietnamese Ports

Abstract

Recently, globalisation has spurred the importance of seaport operations and terminal capacity in the supply chain networks. Seaports play a vital role in the economy of many countries and regions in which ports must be operated in an efficient way for mitigating supply chain risk. Understanding the underlying mechanism of port operation and optimizing profits is essential business objective for port authorities. As matter of fact, port interaction is a nonlinear dynamic process with much complex behavior. Several socioeconomic factors influenced port throughput such as the gross domestic product, fixed assets investments, imports, and exports, etc. Moreover, seaport ecosystems can no longer be the isolated nodes in which they dynamically interact with each other at only a local scale in regional port governance; but within the globally integrated networks of complex supply chains. Thus, it is beneficial to understand the underlying dynamics of complex seaport networks. By analysing chaotic system in port management, the managers can gain better insights into the complex and nonlinear properties of the port interactions in the real market then the optimal management solution can be undertaken. In this dissertation, the port operation system is considered by utilizing four dimensions of the fractional Lotka-Volterra model. First, the dynamic behaviours of the port operation system are investigated by using nonlinear techniques such as equilibrium evaluation, bifurcation, Lyapunov, and time series investigation. The dynamical analysis indicates that the port competition system shows a complex and highly nonlinear behaviour, notably illustrating unstable equilibria and even chaotic phenomena. Next, based on dynamical analysis results, novel multi-criteria decision-making techniques realized by the neural network prediction controller (NNPC) and adaptive fractional-order super-twisting sliding mode control (AFOSTSM) have been utilized for dealing with throughput dynamics under parametric perturbations and external disturbances. Particularly, the active control algorithms are implemented to ensure the recovery strategy for the throughput growth trend of Vietnam ports against system uncertainty and external disturbance. The case study has confirmed the efficacy of the proposed strategy by using system dynamics and control theory. The simulation results show that the average growth rates of container throughput can be ensured up to 7.46% by exploiting the proposed method. The presented method can be also utilized for providing managerial insights and solutions for efficient port operations. In addition, the control strategies with neural network forecasting can help managers obtain timely and cost-effective decision-making policy for port operations against unprecedented disruptions such as the Covid-19 pandemic.