Seaport profit analysis and efficient management strategies under stochastic disruptions

Truong Ngoc Cuong, Hwan-Seong Kim, Le Ngoc Bao Long and Sam-Sang You ()
Additional contact information
Truong Ngoc Cuong: Korea Maritime and Ocean University
Hwan-Seong Kim: Korea Maritime and Ocean University
Le Ngoc Bao Long: Korea Maritime and Ocean University
Sam-Sang You: Korea Maritime and Ocean University

Maritime Economics & Logistics, 2024, vol. 26, issue 2, No 2, 212-240

Abstract: Abstract 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.

Keywords: Port profit and cost; Recurrent neural network (RNN); Data analytics; Stochastic disruption; Hybrid decision support system (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41278-023-00271-z Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:marecl:v:26:y:2024:i:2:d:10.1057_s41278-023-00271-z

Ordering information: This journal article can be ordered from
http://www.springer. ... nt/journal/41278/PS2

DOI: 10.1057/s41278-023-00271-z

Access Statistics for this article

Maritime Economics & Logistics is currently edited by Hercules E. Haralambides

More articles in Maritime Economics & Logistics from Palgrave Macmillan, International Association of Maritime Economists (IAME) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().