A two-stage stochastic optimization model for port infrastructure planning

This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.