A Two Stage Stochastic Optimization Model for Port Infrastructure Planning

TL;DR

This paper presents a two-stage stochastic optimization model for inland port infrastructure planning under demand uncertainty, incorporating costs and transportation decisions, and demonstrates its effectiveness through a case study on the MKARNS system.

Contribution

It introduces a novel stochastic optimization framework for port infrastructure planning that accounts for demand uncertainty and uses an accelerated Benders decomposition algorithm.

Findings

Commodity volume increases with infrastructure investment.

The model identifies key ports for capacity investment.

Stochastic approach improves planning accuracy.

Abstract

This paper investigates inland port infrastructure investment planning under uncertain commodity demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The two-stage stochastic model minimizes the total expected costs, including the capacity expansion investment costs associated with handling equipment and storage, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The Arkansas section of the McCllean-Kerr Arkansas River Navigation System (MKARNS) is used as a testing ground for the model. Results show that commodity volume and, as expected, the percent of that volume that moves via waterways (in ton-miles) increases with increasing investment in port infrastructure. The model is able to identify a cluster of ports that should receive investment in port capacity under any investment scenario. The use of a stochastic approach is justified by calculating the value of the stochastic solution (VSS).