Towards Improved Ship Weather Routing Through Multi-Objective Optimization with High Performance Computing Support

ABSTRACT: Maritime transport remains integral to the global economy, facilitating the cost-efficient and scalable movement of cargo and individuals over varying distances. Modern and effective ship routing solutions not only minimize voyage time and operational costs (including fuel consumption), but also improve resource allocation and environmental sustainability. Their planning process relies heavily on optimization algorithms capable of addressing numerous environmental and operational constraints, particularly in the context of dynamic and often unpredictable weather conditions. A widely adopted approach in the literature is to formulate the ship route optimization problem as a multi-objective optimization (MOO) task, incorporating both static and dynamic constraints. The complexity of this formulation increases significantly when uncertainties related to weather conditions and ship behaviour are introduced, further complicating the optimization process. Meta-heuristic algorithms have gained prominence as effective tools for addressing i.e. complex multi-objective, constrained and nonlinear problems. Despite their demonstrated computational efficiency, the overall process of ship weather route optimization often remains computationally intensive, posing significant challenges for real-time or near-real-time applications in operational maritime contexts. High Performance Computing (HPC) emerges as a viable approach to overcome this limitation. HPC refers mostly to the use of advanced computational systems composed of parallel processing architectures (such as CUDA, OpenMP, MPI, among others) to solve much faster and more efficiently complex and data-intensive problems. Originating in scientific research domains, HPC technologies have rapidly evolved and are now being applied to support solving a wide range of problem in computer science and engineering. Employing HPC-enabled computations allows for designing a scalable and efficient framework for tackling the growing complexity of ship weather routing. This paper discusses the possibilities of HPC integration with MOO for ship weather routing, aiming to demonstrate how HPC-enabled methodologies can improve the performance and real-life applicability of the routing systems.

KEYWORDS: Maritime Safety, Weather Routing, Multi-Objective Optimization, Voyage Planning, Environmental Protection, Artificial Intelligence (AI), High Performance Computing, Optimization Algorithms

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Citation note:

Abdalsalam M., Szłapczyńska J.: Towards Improved Ship Weather Routing Through Multi-Objective Optimization with High Performance Computing Support. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 19, No. 1, doi:10.12716/1001.19.01.12, pp. 93-103, 2025

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