Optimisation of port investment strategies considering uncertain cargo demand and environmental pollutions

• Published in: Computers & industrial engineering Vol. 200; p. 110788
• Main Authors: Lu, Bo, Luo, Rifeng, Wu, Xin, Wang, Huipo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Demand uncertainty; Environmental pollution; Port investment; Stochastic programming; Port investment; Environmental pollution; Demand uncertainty; Stochastic programming
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2024.110788

•Optimization approach for port investment considering uncertain cargo and environment.•A multi-stage stochastic dynamic programming model to formulate the new approach.•The grey system is first used to establish the scenario tree of future freight demand.•Numerical experiments based on real-world cases to draw managerial insights. Port investment is assuming a critical role in contemporary maritime economics, attributable to the escalating demands for enhancing port efficiency and the growing awareness of environmental issues. Currently, numerous ports are operating at full capacity to manage incoming cargoes. Investing in port infrastructure holds the potential to enhance overall port productivity; nevertheless, such investments necessitate substantial financial resources and sufficient time, may encounter limitations due to environmental regulations, and are subject to risks stemming from uncertain demand. Existing investment analysis methods have been utilized to evaluate the impact of uncertainty on port investments, yet they tend to be overly theoretical or lack flexibility in decision-making. In light of these limitations, our study aims to explore a novel approach to optimize port investment strategies considering uncertain cargo demand and environmental concerns, through the development of a multi-stage stochastic dynamic programming model. This model places emphasis on investment choices related to expanding berth capacity, integrating environmental pollution constraints and financial limitations, all while accounting for demand uncertainties. Ultimately, our proposed methodology demonstrates improved analytical capabilities in addressing uncertainty and environmental pollution, as exemplified in a case study.