The global nature of seaport operations makes shipping companies susceptible to potential impacts. Sustainability requires seaport authorities to understand the underlying mechanisms of resilience in a dynamic world, to ensure high performance under disruptions. This paper deals with data analytics for analysing port resilience and a new paradigm for productivity forecasting that utilize a hybrid deep learning method. Nonlinear analytical methods include Lyapunov exponent, entropy analysis, Hurst exponent, and historical event analysis, with statistical significance tests. These approaches have been utilised to show that throughput demand at Busan port (South Korea) exhibits complex behaviour due to business volatility. A new forecasting method based on long short-term memory (LSTM) and random forest (RF) has been applied to explore port throughput in realizing recovery policy. The LSTM networks have shown high effectiveness in time-series forecasting tasks; RF is proposed as a complementary method to mitigate residual errors from the LSTM scheme. Statistical significance tests have been conducted to comprehensively evaluate the introduced forecasting models. The results show that the hybrid method outperformed three benchmarked models in both the short- and long-term forecasting at a 95% confidence level, guaranteeing accuracy and robustness as well as suitability. As a seeking strategy for seaport competitiveness, novel resilience planning incorporates sustainability to prepare for disruptions such as a global pandemic.

Managing highly evolving supply chains can be challenging, especially when vulnerable to disruptions and risks. This paper deals with a supply chain system’s dynamical analysis and efficient management strategy using a four-stage hyperchaotic Lorenz–Stenflo equation under disruptive events. 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 negatively influencing the performance of various stages of multi-echelon supply chains. Resilient supply chains have been developed along with dynamic identification by realizing an adaptive fractional-order controller. An efficient control algorithm can optimize the management system while reducing potential risks by employing control theory in a decision support system. Performance criteria have been exploited to validate the control methodology. Using digital management algorithms, decision-makers might effectively cope with chaos suppression and synchronization problems, ensuring productivity and sustainability. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.

Container terminal automation offers many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A novel lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure fast and accurate lane positioning. A robust lane detection method can deal with complicated detection problems in realistic road scenarios. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions; in addition, the robustness of the lane detection method with stochastic noise is tested.