Seaport profit analysis and efficient management strategies under stochastic disruptions

This study deals with managing supply chain costs and profit for ports’ hinterland shipments and container transshipment under stochastic disruptions. The underlying mechanisms describing port economic activity are explored from a transport chain perspective. A dynamic analysis of port competition under supply chain disruptions is conducted using nonlinear data analytics. The supply chain profits demonstrate a complex and highly nonlinear behavior, characterized by inherent instability. Port costs and profit are influenced not only by transshipment and hinterland shipments, but also by many types of other factors. A hybrid decision support system for managing supply chain profit is presented here, integrating recurrent neural networks (RNNs) with a fractional-order sliding mode controller (FOSMC). The hybrid method is implemented to approximate the unknown profit function and eliminate supply chain disruptions, using a deep learning estimator. Based on case studies of the Busan and Incheon ports of South Korea, the numerical test scenarios demonstrate the potential benefits of port operators from the proposed management strategy. Subsequently, the hybrid management system, created by RNN and FOSMC, enables port authorities to reduce operating costs and improve profitability, thereby enhancing competitiveness and resilience. The presented methods can be used to provide managerial insights and solutions to port authorities, under supply chain disruptions. Our novel decision support system can help policymakers develop a timely and cost-effective decision-making strategy, aiming at port resilience and sustainability against the potential impacts on global supply chains.