Risk-aware stochastic ship routing using constrained continuous belief tree search
Year: 2024
Type: article
Abstract: Improved route planning for commercial shipping can enable reduced environmental impact, improve ship safety records, and lower fuel and maintenance costs. A fundamental challenge is to design ship routing algorithms that can contend with uncertain weather forecasts and real-world models of ship performance and safety. This paper introduces a stochastic ship routing framework that uses the conditional value-at-risk (CVaR) metric to guide the behaviour of a modified Continuous Belief Tree Search (CBTS) algorithm to find a safe and fuel-efficient long-haul shipping route. Our method provides a principled means to utilise a probabilistic representation of weather forecasts derived from ensemble forecasting for the purpose of route planning and allows for a user-defined threshold of risk tolerance. Another key advantage of our method is its ability to dynamically choose candidate route waypoints using weather-dependent estimates of fuel and safety information. Evaluation of long-haul routes through the Atlantic, Pacific and Indian oceans using real-world ship performance models and weather forecasts show considerable improvements in fuel usage and computation time compared to state-of-the-art ship routing algorithms. • This paper introduces and solves a novel stochastic ship weather routing problem. • The method finds low-fuel, low-risk, on-time routes through uncertain weather. • On real ship and weather data, our method uses 2.5-13% less fuel than others. • The method takes 5-30% less time to compute than other algorithms.
Source: Ocean Engineering
Institution: University of Technology Sydney
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,030
OpenAlex ID: W4404043409