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

作     者：Lu, Bo Luo, Rifeng Wu, Xin Wang, Huipo 

作者机构：Dalian Univ Technol Sch Econ & Management Dalian 116024 Peoples R China Hebei Univ Engn Sch Management Engn & Business Handan 056038 Peoples R China 

出 版 物：《COMPUTERS & INDUSTRIAL ENGINEERING》 (Comput Ind Eng)

年 卷 期：2025年第200卷

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Key Project of Philosophy and Social Science Foundation of China [20 ZD129] National Natural Science Foundation of China [72073018, 72261147705, 42471197] 

主　　题：Port investment Demand uncertainty Environmental pollution Stochastic programming 

摘      要：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 multistage 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.