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1 INTRODUCTION
Maritime transport is the backbone of global logistics,
is heavily relied upon to ship goods between countries
and regions[1]. It also enhances national
competitiveness by integrating countries into global
trade networks. In recent years, trade and logistics
exchanges have grown. According to the International
Maritime Organization (IMO), around 11 billion tons
of goods are transported by sea annually, accounting
for over 80% of world trade by volume and more than
70% by value[2]. This makes it the dominant mode of
Towards Improved Ship Weather Routing Through
Multi-Objective Optimization with High Performance
Computing Support
M. Abdalsalam & J. Szłapczyńska
Gdansk University of Technology, Gdańsk, Poland
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.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 19
Number 1
March 2025
DOI: 10.12716/1001.19.01.12
94
international trade due to its superior capacity to
handle large quantities of goods at low costs[3].
However, it faces various challenges that affect its
efficiency and effectiveness. A key issue is the rising
cost of fuel due to global price fluctuations, which
increases operating expenses. The sector also faces
growing environmental pressures, including
significant demands to reduce carbon emissions
doubts regarding alternative fuel options. Maritime
shipping accounts for approximately 3% of global
carbon dioxide emissions[1]. As shown in Figure 1,
since 1970, shipping emissions have more than
doubled, rising from about 350 million to over 700
million metric tons by 2023[4]. According to Statista,
the shipping sector accounted for 12% of transport-
related CO₂ emissions in 2023, with 4.5% from
domestic and 7.5% from international shipping[5].
The situation necessitates a shift toward more
sustainable modes and technologies to reduce negative
environmental impacts. On the other hand, climate
change and extreme weather disrupted shipping
operations, affecting global supply chains and
increasing accident risks.
(a)
(b)
Figure 1. Environmental impact of shipping [4]. a) Carbon
dioxide emissions from international shipping worldwide
(19702023, million metric tons); b) Distribution of carbon
dioxide emissions by transportation subsector worldwide
(2023)
These factors highlight the need for effective
measures to ensure safety and minimize weather-
related disruptions. Considering these growing
environmental and climate challenges, the need for
innovative solutions that address emissions and safety
challenges while enhancing efficiency and reducing
costs has emerged. Among the most prominent of these
solutions is the development of advanced vessel
routing systems that take weather conditions into
account, simultaneously improving efficiency and
reducing fuel consumption. This is where technologies
that determine optimal ship routes based on accurate
weather data come in. These technologies contribute to
reducing fuel consumption, thus reducing emissions
and costs, while enhancing maritime safety by
avoiding hazardous weather conditions.
Ship weather routing has gained attention in both
academic and industrial circles. It primarily aims to
improve voyage profitability and ensure the safety of
ships and crews. Weather routing optimization
identifies the best ship routes, balancing conflicting
goals such as reducing fuel use-which may lead to
unsafe speeds in rough weather-and ensuring safety,
which might increase travel time and emissions. It also
facilitates the optimization of ship operational
parameters to achieve a balance between speed, fuel
efficiency, dynamic loading, and the influence of
weather conditions. The goal is to minimize voyage
time, fuel usage, and safety risks, rather than to
increase speed directly (which is limited by wave
impact) [6]. Voyage optimization helps shipping
companies to lower costs and improve
competitiveness. Efficient operation across cost,
energy, and safety is essential. Many vessels now a
days use weather routing systems, which rely on
meteorological and oceanographic data, ship
characteristics, and route information. Depending on
operator goals, optimization may focus on energy
efficiency, travel time, safety, or a mix of these. The
need for quick, effective decisions is growing due to
fast-changing weather and navigation conditions. This
calls for advanced computational methods to improve
efficiency and support fast, reliable planning [7].
The main goals of weather routing are often in
conflict, making MOO crucial to find the best trade-
offs. This process is resource-intensive and requires
analyzing large datasetsranging from weather
forecasts and vessel specifications to environmental
limits. Modern routing solutions aim to reduce travel
time and costs while improving sustainability and
resource use. These solutions depend on optimization
algorithms that can handle both environmental and
operational constraints, especially under dynamic and
unpredictable weather. A common method in the
literature is to model routing as an MOO problem [8],
incorporating both static (land and shallow waters)
and dynamic (e.g. regions of high waves) constraints.
The challenge increases with uncertainties in weather
and vessel behavior, which make the optimization
process far more complex. Metaheuristic algorithms
are widely used to solve complex, constrained [9], and
nonlinear MOO problems. Although effective, the
optimization of ship routes under weather constraints
remains computationally intensive, posing challenges
for real-time or near-real-time applications.
Recently, High-Performance Computing (HPC) has
become important for solving complex problems in
science, engineering, and data analysis. It enables faster
simulations, supports breakthroughs in Artificial
Intelligence (AI), and helps researchers process
massive datasets. The success of HPC is counting on
multidisciplinary efforts, including supercomputers,
parallel programming, network-based HPC
environments, and HPC applications. HPC uses
advanced systems with parallel processing (e.g.,
Compute Unified Device Architecture (CUDA), Open
Multi-Processing (OpenMP), Message Passing
Interface(MPI) to solve complex, data-heavy
problems much faster[10]. Originally used in scientific
research, HPC is now applied in many computer
science and engineering fields.
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The ship weather routing models need to process
huge amounts of data and a heavy workload with the
requirement of ultra-fast response. Therefore, it is of
extreme necessity to promote computation efficiency
by applying HPC to ship weather routing systems.
Using HPC allows the design of scalable and efficient
frameworks to manage the growing complexity of
weather routing.
This paper tries to outline how HPC could be
integrated with multi-objective optimization for more
efficient ship routing. It aims to show how HPC can
improve the performance and practical use of routing
systems. The rest of this paper is structured as follows:
the next two sections discuss related work on Ship
Weather Routing (SWR), including its Multi-Objective
Optimization (MOO) variants, and High Performance
Computing (HPC). The following section introduces
the few existing s.o.t.a. research on combined SWR and
HPC. Then in the next section we identify research
gaps and the need for further research for utilizing
HPC in this context. The following section outlines a
framework for HPC-based SWR solution. Finally, the
last section concludes the material presented,
including also some recommendations for future
research.
2 SHIP WEATHER ROUTING (SWR)
Voyage planning is a fundamental part of safe ship
navigation. It helps avoid risks in areas with high
maritime traffic, shallow waters, or unfavorable
weather conditions[11]. The IMO requires a
comprehensive voyage plan approved before
departure. This plan must address routing systems,
environmental protection, and safety in line with the
Safety of Life at Sea (SOLAS) convention[12]. Routing
systemssuch as Traffic Separation Schemes (TSS),
precautionary areas, and zones to avoidsupport
navigational safety and environmental compliance.
However, static hazards alone do not define routing
challenges. Dynamic meteorological and
oceanographic conditions also play a major role, with
waves, winds, and currents directly affecting fuel use,
speed, and route safety.
Ship weather routing research typically falls into
two groups: global path planning, which seeks an
optimal route between origin and destination based on
environmental data, and local path planning, which
focuses on short-term adjustments. Early log-term
(global) weather routing methods relied on the
isochrone algorithm introduced in 1957 [13]. It
calculates the furthest distance a ship can travel in
equal time intervals, assuming fixed environmental
conditions. While simple and intuitive, when
implemented as a computer algorithm it introduced
issues such as isochronal loops. Hagiwara [14]
addressed this by using sector-based waypoint
expansion and pruning suboptimal subsectors. Later,
Lin [15] proposed a three-dimensional variant using a
dynamic grid, and Chen et al. [16] improved it further
with the Isochrone-A* method, which dynamically
adjusts the search space and uses an augmented cost
function for multi-objective optimization.
With the rise of digital charts and computational
capacity, graph-based algorithms such as Dijkstra’s
[17] have become widely adopted. These algorithms
model sea areas as graphs, with edge weights adjusted
for weather-induced speed losses. Sen and Padhy [17]
introduced weights based on reduced speeds due to
wind and waves. Takashima et al. [18] adapted
Dijkstra’s algorithm for fuel savings in coastal
navigation by adjusting propeller revolutions.
However, standard Dijkstra paths are often not smooth
and may lack flexibility. Modifications have
introduced features such as collision avoidance[19],
MOO [20], and time-dependent variables. These
enhancements improve applicability but do not resolve
limitations in path realism and continuous control.
Dynamic programming (DP) offered an alternative,
particularly for grid-based optimization. In early DP
approaches to weather routing De Wit[21] and Calvert
et al. [22] applied two-dimensional DP (2DDP) to
optimize route selection under static ship settings. This
approach divides the sailing region into a spatial grid
and uses recursive optimization over time steps. To
capture more realistic behaviors, later studies extended
this to three-dimensional DP (3DDP), including control
variables such as speed and engine power. Wei and
Zhou[5] reduced the high computational demand of
3DDP by clustering similar control states. Still, DP-
based methods remain demanding, especially when re-
optimization is needed due to real-time changes in
weather or vessel status.
3 MULTI-OBJECTIVE OPTIMIZATION (MOO) IN
THE CONTEXT OF SWR
In recent years, Multi-Objective Optimization (MOO)
has gained more attention, also in the context of ship
weather routing, due to its ability of balancing the
complex trade-offs between conflicting goals (in SWR:
fuel efficiency, voyage time, emissions, and safety).
While traditional methods remain as foundational
approaches, they have been adapted to handle
multiple goals- modified classical algorithms that
generate Pareto-optimal solutions to advanced
evolutionary algorithms tailored for simultaneous
optimization of time, cost, safety, and emissions. In this
context, MOOparticularly in hybrid formshas
demonstrated effectiveness, the gains associated with
the speed of classical methods with the flexibility of
modern algorithms. These hybrid models usually rely
on traditional methods such as Dijkstra[23], isochrones
or dynamic programming to generate initial routes or
perform local searches while using metaheuristics to
explore complex solution spaces.
Numerous MOO-based approaches and hybrid
strategies have been introduced to determine the
optimal route and support solution diversity. For
example, improved multi-objective ant colony
optimization (IMACO) [24] combined with the
Technique for Order Preference by Similarity to Ideal
Solution (TOPSIS) has shown potential in balancing
navigation risk and fuel consumption. Similarly,
hybrid versions of Particle Swarm Optimization (PSO)
and Genetic Algorithms (GA), enhanced with
heuristics, have been used to optimize ship fuel
consumption mode and routes under environmental
constraints[25]. Researchers have also customized the
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Estimation of Distribution Algorithm (EDA) [26] for
planning ship routes in a non-stationary
environment (tidal water) with multiple goals by using
probabilistic models of the best solutions instead of the
usual crossover and mutation methods. In practice,
EDA explores the solution space by sampling, selecting
strong route candidates, and building probability
distributions that reflect their key traits. This method
avoids early convergence to weak solutions by keeping
a diverse set of options, handles trade-offs between fuel
and time more effectively, and adapts well to uncertain
weather. Evolutionary algorithms, especially the Non-
dominated Sorting Genetic Algorithm II (NSGA-
II)[27], have been also applied. These methods provide
Pareto-optimal solutions that help operators select
routes based on specific preferences. The Multi-
Objective Evolutionary Algorithm based on
Decomposition (MOEA/D) further improves
performance by dividing the problem using weight
vectors. An advanced version, w-MOEA/D[28] which
applies w-dominance[29] to standard MOEA/D
algorithm, uses ensemble forecasts for uncertainty
management, and accounts for real-world constraints
such as bathymetry and emission zones. Despite these
advantages, applying an MOO-based and hybrid
approach in ship weather routing is computationally
high and time-consuming. Evaluating multiple
objectives requires searching a large multidimensional
solution space and running various scenarios under
varying weather conditions, which can significantly
increase computation time. As a result, real-time or
near-real-time application of MOO remains difficult
without additional support.
Recent studies have introduced also Machine
Learning (ML) and hybrid methods to improve
weather routing performance. ML has been used to
enhance heuristic and meta-heuristic functions in
classic algorithms, guiding the search toward better
candidate routes. For example, Chen and Mao [30]
incorporated ML-based cost predictions into the
Isochrone method to improve waypoint selection and
route smoothness. Moreover, Shin et al.[31] proposed
an improved A* algorithm for ship routing that
integrates AIS and marine weather data to address
limitations in existing methods. They introduced an
adaptive grid system to reduce computational
complexity and applied a 16-way search strategy to
generate smoother and more economical routes.
Machine learning models were used to estimate the
ETA and evaluate route efficiency. Simulation results
showed that their proposed method outperformed
traditional A* routing by providing more economical
paths. Hybrid methods often combine exact or
heuristic algorithms with metaheuristics to balance
quality and speed. In this regards, Qian et al. [32]
integrated A* with genetic algorithms to handle speed
variations over a dynamic grid. Similarly, Ma et al. [33]
combined A* with an improved NSGA-III to support
MOO under environmental and operational
constraints. These approaches leverage the strengths of
different algorithmsusing graph search for reliable
structure and evolutionary methods for flexible
optimization. However, their performance still
depends on the quality of initial solutions and the
tuning of ML models, especially under uncertain and
changing sea states.
Despite these improvements, no single method
provides an optimal solution covering all cases.
Heuristic and graph-based approaches provide speed
but often ignore multi-objective trade-offs. Dynamic
Programming (DP) provides global optima; however,
it sacrifices scalability. Metaheuristics handle multiple
objectives and constraints but can suffer from long
convergence times and uncertain performance in
dynamic settings. To benchmark and improve these
approximations, VISIR [34] and its successor[35]
provide reference solutions by solving the governing
equations of motion under realistic weather conditions.
VISIR-2 improves the model by adding factors such as
wave angles, wind and current directions, and fuel
emissions, which helps in finding routes that produce
the least CO₂ and assists researchers in checking how
accurate the approximate methods are. Recently,
researchers' efforts have shown that ship weather
routing has progressed beyond simple time or fuel
minimization to advanced multi-objective
optimization. Modern methods integrate route
planning with control parameters such as speed,
power, and trim while accounting for changing
weather conditions and vessel behavior. Higher-
resolution environmental data and ensemble forecasts
have improved accuracy but increased the size of input
data[28]. As a result, algorithms must handle larger
solution spaces and evaluate more variables, which
leads to higher memory usage and longer runtimes.
This requirement is especially critical in methods such
as 3D dynamic programming or population-based
algorithms that evaluate many control states at each
step. Machine learning and hybrid approaches arise to
guide search and improve performance. However,
implementation of it in real-time use remains limited
due to computational costs and the need for dynamic
updates during long voyages. Clean fuel ships and
new propulsion types have more variables to optimize,
which makes the problem even harder. Benchmark
studies are still rare, which makes comparing methods
and validating improvements difficult.
A potential solution able to solve the complexity,
high dimensions, and memory requirements of MOO
problems in ship weather routing is HPC. It provides
an approach to splitting tasks across many processors
or GPUs; HPC reduces run times and manages the
increasing memory requirements. It lets models
evaluate more route options and weather scenarios in
near real time.
4 OVERVIEW OF HIGH PERFORMANCE
COMPUTING (HPC)
Recently, HPC has become an essential tool across
scientific communities. A vast increase in the use of
HPC has been observed since its origins in the mid-
20th century, and it has become essential in modern
systems. Researchers struggle with computational
problems when their problem needs a huge
computation and memory resources. Since typical
desktop computers do not work efficiently compared
with HPC, the result is usually low-performance
computing. HPC plays a key role in accelerating
computational tasks and enabling large-scale data
analysis across various fields. In practice, the concept
of accelerating computation includes load balancing
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across processors, optimized storage systems, and
efficient data processing. In this regard, the term
speedup in this context (e.g., in parallel computing),
refers to the speed at which a task can operate when
divided among multiple processors. Amdahl’s Law
clarifies that the non-parallel part of a program sets a
limit on the total gain. For example, if 10% of the
process cannot truly run in parallel (the process runs
only sequentially), no amount of added processors can
reduce the overall time below that 10%. This
phenomenon makes sequential computing processes
tasks one after the othera bottleneck. A bottleneck is
something that slows down the overall processsuch
as a narrow point in a bottle that restricts flow.
Moreover, the serial code represents the narrow point.
In contrast, Gustafson’s Law justifies that scaling the
problem size with the number of processors can keep
them fully utilized. As a result, a complex problem can
be solved in the same time by using more processors,
which helps reduce the impact of the serial portion.
These ideas relate to strong and weak scaling in
HPC; the terms "weak scaling" and "strong scaling"
refer to how well a system can maintain its
performance as the number of processors or nodes is
increased. Strong scaling looks at performance when
the problem size stays fixed but the processor count
grows. Weak scaling tests if performance holds steady
when both the problem size and processor count grow
together. These model help guide parallel algorithm
design by highlighting trade-offs between speed,
efficiency, and processor usage. That explains why
HPC is considered a major leap in computing, offering
the power to solve complex problems more efficiently
and unlock new capabilities. HPC architecture takes
many forms based on users' needs. Researchers can
choose different ways to design HPC systems.
Designing an HPC system may involve a combination
of parallel computing, cluster computing, and
grid/distributed computing strategies. HPC
architectures are commonly categorized into three
main components: compute, network, and storage.
These components work together to enable high-speed
processing of complex tasks.
Beyond these, systems also differ in how processors
and memory interact. Memory-architecture models fall
into three categories:
1. Shared-memory systems
All processor cores access a single, global address
space. Threads coordinate through lock or barrier
primitives and share data via common variables.
2. Distributed-memory systems
Each computing node has its own private memory.
Nodes exchange data by passing explicit messages
over an interconnect (for example, using MPI).
3. Hybrid systems
Nodes combine multicore CPUs and accelerators
(GPUs) and use both shared-memory and message-
passing techniques. Within a node, threads share
memory; between nodes, processes communicate
by messaging.
Each model aligns with different programming
frameworks (e.g., OpenMP for shared memory, MPI
for distributed memory, CUDA or OpenCL for GPU
acceleration). Efficient use of shared, distributed, and
hybrid memory architectures depends on choosing the
right parallel framework. The following table (Table 1)
summarizes common frameworks and their typical use
cases.
Table 1- HPC Programming Frameworks
Framework
Target
Hardware
Language
Support
Use Case
MPI
CPU clusters
C, Fortran,
Python
Large-scale
messaging
OpenMP
Multi-core
CPU
C, C++,
Fortran
Loop-level
parallelism
CUDA
NVIDIA
GPUs
C/C++
Data-parallel
tasks
OpenCL
GPUs, CPUs
C
Portable
kernels
OpenACC
GPUs
C, Fortran
Simplified
GPU use
As mentioned above, the HPC relies on splitting
tasks into smaller parts that can run at the same time
through applying several technologies that are used to
implement and create high-performance computing
systems. Parallel processing is a method of running
more than one processor to handle separate parts of a
single task. Dividing different parts of a task among
multiple processors minimizes the time required,
achieves lower power consumption, and enables more
efficient multitasking of the program.
Most systems with more than one central
processing unit (CPU) are able to perform parallel
processing, in addition to the multi-core processors
commonly found on computers today. Another
method uses GPUs, which contain thousands of
lightweight cores designed to handle many repeated
tasks in parallel. Moreover, SIMD (Single Instruction,
Multiple Data) is another technique used in CPUs; it
allows one instruction to process many data points at
once, often speeding up loops. Some advanced models
have incredible portability across various computer
architectures and operating systems, such as MPI
(Message Passing Interface). MPI allows several
computers to work simultaneously on a problem by
exchanging data across a network. MPI is common in
large simulations run on clusters. On the other hand,
the OpenMP API supports multi-platform shared-
memory parallel programming in C/C++ and Fortran.
The OpenMP API defines a portable, highly scalable
model.
Furthermore, heterogeneous systems combine
CPUs, GPUs, and other accelerators. This setup
balances flexibility and speed by allowing each device
to handle the parts it performs best on. These models
form the core of HPC and make it possible to solve
complex problems faster and at larger scales.
OpenMP and PThreads are used for multithreading
on shared memory architectures. CUDA and OpenCL
enable GPU programming, whereas OpenACC
simplifies the use of GPUs through compiler directives.
MPI is the de facto standard for message passing,
whereas OpenSHMEM provides a shared memory
programming model in distributed systems. Libraries
such as Apache Hadoop and Spark support large-scale
data processing based on a task-based and dataflow
abstraction model. HPC programming requires trade-
offs between low-level control and high-level
abstraction. Lower-level interfaces such as MPI offer
fine-grained control at the expense of requiring more
intricate development. Higher-level programming
models simplify development, possibly at the cost of
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performance. Recent developments indicate a shift
toward hybrid parallelism, energy-efficient
computing, and emerging memory technologies such
as NVRAM. These trends aim to increase efficiency and
better suit emerging workloads such as AI applications
and real-time analytics.
4.1 High-performance computing (HPC) architectures
HPC architectures include a broad range of hardware
configurations designed to solve complex problems
and perform intensive computational tasks.
Traditional HPC clusters usually consist of
homogeneous compute nodes connected through
high-speed, low-latency. HPC systems support
multiple layers of parallelism, from instruction-level
parallelism within processor cores to node-level
parallelism across clusters. One can then organize them
as a hierarchical structure of parallelism categories that
spans from the lowest level (instruction-level within a
single core) through intermediate levels (thread-level,
core-level) to the highest levels (node-level, cluster-
level). Along with that, most HPC systems also contain
complex memory hierarchies, including several cache
levels, local memory, and often shared or distributed
memory. Shared memory within a node enables rapid
data exchange between threads but requires careful
coordination to avoid race conditions. This architecture
demands close attention to data locality and movement
since performance degrades quickly when data must
travel farther from the processor. Figure 2 presents a
three-tiered view of HPC architectures that includes, at
the top, the Parallel Programming Model layer, which
encompasses message-passing (MPI) for process-level
coordination and shared-memory/threading
approaches (OpenMP/PGAS) for intra-node execution;
the middle Middleware layer comprises
communication libraries (MPI/PGAS/RDMA), job
scheduling and resource managers (e.g., Slurm,
Torque, Sun Grid Engine), and parallel file systems
(Lustre, NFS, GPFS) that connect compute tasks and
data movement across nodes; and at the base, the
Infrastructure layer includes physical compute engines
(CPUs, GPGPUs, FPGAs), network fabrics
(Infiniband/Ethernet, iSCSI/FC), operating systems
with numerical and accelerator libraries
(BLAS/LAPACK, CUDA/OpenCL), and centralized
storage arrays (SAN/NAS), illustrating how hardware,
system software, and programming frameworks
interlock to drive large-scale scientific applications.
Recently, graphical processing units (GPUs) have
become a major component of modern HPC systems.
The emergence of Graphics Processing Units (GPUs)
transformed HPC architecture. Originally built for
rendering graphics, GPUs evolved into general-
purpose accelerators. With thousands of simple cores,
they handle data-parallel workloads efficiently. When
aligned with suitable algorithms, GPUs can greatly
outperform CPUs. Today, many of HPC computer
systems are characterized by their both CPUs and
GPUs to match different computational demands. This
hybrid model boosts performance in fields such as
deep learning, molecular dynamics, and
computational fluid dynamics and computational fluid
dynamics. However, software must be tuned to the
GPU’s execution model to exploit its full potential.
Figure 2. HPC architecture[36]
Widely used frameworks such as MPI, OpenMP,
and CUDA provide fine-grained control over
resources. Moreover, alternatives techniques such as
OpenCL and Heterogeneous-compute Interface for
Portability (HIP) enable code portability across
different hardware. Other techniques, including Data
Parallel C++ (DPC++) and C++ Single-source
Heterogeneous Programming for OpenCL (SYCL),
offer modern C++-based models for writing portable
parallel code. Directive-based approaches such as
Open Accelerators (OpenACC) simplify GPU adoption
without major code changes. Additional libraries such
as Performance Portability Programming EcoSystem
(Kokkos) and RAJA Performance Portability Layer
(RAJA) support writing portable code across CPUs and
GPUs. Such programming models help developers
optimize hybrid HPC systems while maintaining
flexibility across architectures.
4.2 Implementation of High-Performance Computing in
different domain
Today, HPC systems are revolutionizing scientific
research by providing the power needed to tackle
complex problems across many fields. These systems
let researchers process large datasets, run detailed
simulations, and build predictive models with
unmatched speed and accuracy. Engineers use it for
mechanical design, device testing, and product
development, minimizing costs and increasing
efficiency. In the automotive sector, HPC drives digital
simulations and safety assessments. Energy companies
apply it to reservoir modelling and subsurface geology,
evaluating resource potential and estimating costs.
Despite its broad adoption, HPC remains
underused in maritime applicationsespecially ship
weather routing. Most routing systems still rely on
sequential programming or on low-parallelism
methods that have been implemented on some
algorithms, such as genetic algorithms or graph-based
searches (A*, Dijkstra). On the other hand, related
maritime domains, such as coastal flooding, vessel
tracking, and ship-ice modelling, have seen some HPC
use, but ship routing lags. Ship routing under dynamic
weather conditions demands frequent recalculations of
forecasts, wave states, wind directions, and ship
constraints.
Recent studies highlight HPC’s potential impact on
maritime research. For example, a researcher in
cooperation with the National Supercomputing Centre
ran full-order fluidstructure interaction simulations
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on a 14.4 million-cell [37]. They used the PETSc library
an open-source toolkit for solving large-scale
scientific problems and a GMRES solver to solve big,
complex problems at the same time on five computers,
(typical multi-core desktop ones, with 24 cores, using
Intel® Xeo E5-2690 v3 CPUs (2.60 GHz). This
configuration reduced runtimes from days to mere
hours. It let researchers sweep through dozens of
design parameters in one campaignsomething
impossible on smaller clusters. By handling high cell
counts and fine time steps, the simulations captured
millimeter-scale wave features and non-linear wave
wave interactions. Building on these simulations, the
team developed data-driven digital twins of ships and
offshore structures. The twins run in near real time,
ingesting live environmental data to predict structural
responses under changing loads. Such examples
emphasize the ability of HPCs to run ensemble
forecasts, whereas uncertainty quantification lets
operators explore multiple scenarios in minutes. This
capability supports the adaptive control of wave
paddles in test basins and, ultimately, onboard
decision support for vessels at sea. Yet most ship
routing systems stick to legacy solvers, even though
modern parallel methods could boost performance;
they still rarely use these advances. The enhanced ship
routing model should implement HPC strategies such
as combining distributed computing for broad tasks,
shared-memory threads for local work, and hardware
accelerators for compute-intensive operations.
The maritime sector now faces rapid changes due to
digitization, decarbonization, and safety demands. As
ships tap real-time forecasts and IoT data, computing
needs will surge. HPC could address these needs by
enabling live, multi-objective route planning that
balances safety, fuel use, and transit time. Traditional
methods remain dominant in current systems;
enhanced parallel computing approaches offer
significant untapped potential. However, it should be
noted that developing HPC solutions is not easy; it
demands deep knowledge of parallel programming,
memory management, and hardware architecture. To
fully utilize HPC, developers must create routing
algorithms that are specifically designed to take
advantage of the latest parallel hardware and
programming models. Many developers avoid HPC
methods because they demand deep expertise in
parallel programming and system optimization.
Writing code for multi-level parallelism requires
understanding of memory models, concurrency, and
hardware architecture. These challenges raise
development costs and extend project timelines as a
result, teams stick to simpler sequential or limited-
implementation.
5 SHIP WEATHER ROUTING TECHNIQUES
BASED ON HPC
Multi-objective ship weather routing balances safety,
fuel use, and voyage time by treating each as an
optimization objective. The process evolves route
candidates over many generations. Each route is
evaluated on passage time, fuel consumption, and
safety, yielding a set of non-dominated solutions
known as the Pareto front. The voyage is divided into
stages, and at each stage the algorithm records the best
trade-off among objectives for each state. This process
builds a map of optimal choices. To compare options,
the objectives might be combined into a single score
using user-defined weights, but thus the solution may
overlook some Pareto-optimal routes.
Ship weather routing is computationally complex
because environmental conditions change dynamically
(in space and time). Wind, waves, and currents might
be represented on a high-resolution grid. Each grid
point holds forecasts for certain number of hours.
Evaluating a route requires sampling forecasts at each
step and multiplying data readings. As a result,
datasets can span thousands of latitudelongitude cells
over dozens of time steps and require gigabytes of
RAM. Tracking constitutes hundreds of route
candidates across multiple weather scenarios can push
memory needs into tens of gigabytes. These demands
make the problem a clear fit for HPC. Indeed, HPC
meets these demands by rapidly processing large
datasets and executing routing algorithms in parallel.
It supports the conflicting goals of travel time, fuel use,
and safety under unpredictable sea conditions [38].
Using HPC, one can evaluate numerous route options
with high-resolution environmental data in real time
[39]. Traditional serial methods cannot meet this
requirement. A key advantageespecially with GPU
accelerationis parallelizing route evaluations. For
example, implementing Non-local Kernel Density
Estimation (NLKDE) on GPUs enabled real-time
trajectory visualization of AIS data [40]. Similar
methods can be applied to ship routing by evaluating
cost functions over thousands of paths using
multithreading, GPU or FPGA accelerators, and SIMD
architectures. Models such as OpenMP and CUDA
support maritime applications, allowing tasks such as
segment-based risk analysis or energy prediction to
run in parallel.
Integrating HPC into ship routing reduces
simulation time, improves accuracy through higher-
resolution data, and supports real-time evaluation of
diverse options and constraints. It also addresses
growing computational needs in the maritime sector. A
notable example is the Sailing Assistance Application
(SAA) by Życzkowski et al[19]. the first parallel
implementation of deterministic weather routing for
sailboats. Deployed in a cloud environment with
OpenMP, SAA achieved up to 79.1% speedup in
routing components while preserving accuracy. It
divided tasks into navigation area generation and
sailing condition modeling using shared-memory
architectures. However, SAA focuses on sail-assisted
vessels, not the dominant mode of commercial
shipping and uses Dijkstra’s algorithm, which is less
optimal for large-scale or multi-objective problems.
Their proposed method implements HPC techniques
only through OpenMP, which means it does not take
advantage of other HPC techniques. Choosing HPC
techniques requires analyzing various options, such as
GPU acceleration or distributed memory models
(MPI), which makes the proposed approach more
difficult to scale for large areas and high-resolution
grids.
To generalize and improve HPC-based routing, one
might adopt a vessel model that supports a generalized
engine-driven ships. A multi-level parallelism
framework combining MPI, OpenMP, and GPU
acceleration would enhance scalability.
100
Algorithmically, parallelized multi-objective solver
can better match real-world needs. Dynamic rerouting
and adaptive grid resolution would enable responsive
and efficient navigation. These enhancements would
yield a versatile, high-performance system across
vessel types and scenarios. As presented in another
study[32] Segment Parallel A* (SPA*) was presented.
SPA* applies HPC principles to optimize maritime
path planning under complex environmental
conditions. It divides the route into sub-routes and
distributes computation across multiple CPU cores
using MATLAB.
Each segment uses an enhanced A* algorithm that
balances risk, distance, and speedups from (as reported
by the Authors) five to over 12,425 times compared to
Dijkstra, A*, Bidirectional A*, Ant Colony
Optimization, Harris Hawks Optimization, and
Sparrow Search Algorithm. SPA* efficiently handles
high-resolution, multi-constraint data, including real-
time wind, wave, and topographic risk inputs. Yet it
relies solely on CPU parallelism and grid-based
models, which may incur overhead at high resolutions
or from excessive segmentation.
Most prior ship routing methods remain sequential,
even though parallel techniques have been applied in
related domains such as swarm-based optimization
[41][42][43][44], graph computations [45], and robotic
motion planning [46][47]. Integrating HPC with
deterministic routing may reduce execution time, but it
limits flexibility for multi-objective and real-time
applications. In contrast, parallelizing metaheuristic
MOO algorithmssuch as NSGA-II, NSGA-III, or
MOEA/Doffers a scalable alternative for full-scale
ship weather routing. These methods can better exploit
HPC, support dynamic constraints, and enable faster
decision-making with broader optimization goals. A
deterministic approach, in this context, has limited
applicability.
6 THE LITERATURES GAP
The literature review presented in the previous
sections revealed that ship weather routing has gained
renewed interest recently as regulators and operators
press to cut emissions and boost efficiency. Scholars
have shifted their focus to ship weather routing, which
employs multi-objective optimization to balance
competing goals under varying weather conditions.
But multi-objective ship weather routing significantly
increases computational complexity compared to
single-objective optimization. The multi-dimensional
optimization space generates exponential growth in
computational requirements; it often exceeds the
available decision windows in practical navigation
scenarios. This "curse of dimensionality" becomes
particularly problematic when considering high-
resolution environmental data needed for accurate
routing. Moreover, ship weather routing requires
timely decision-making, especially in dynamic weather
conditions, which create significant limitations for
providing real-time solutions to complex multi-
objective routing problems. State-of-the-art methods
can find globally optimal solutions under specific
conditions. However, recent reviews highlight the
increasing complexity of these methods as it handle
multiple objectives, which increases computational
demands[48]. While they offer strong guarantees, their
efficiency drops with large-scale or dynamic problems.
This becomes a key limitation in ship routing, where
weather and ocean conditions change frequently and
require continuous re-optimization.
These challenges have revealed HPC’s potential for
overcoming them and paved the way for broader use.
Despite these developments, HPC remains
underutilized in weather-based ship navigation so far.
The literature review identified several significant
research gaps at the intersection of HPC and multi-
objective ship weather routing. Many ship routing
algorithms do not effectively utilize the HPC
techniques, such as parallel computing capabilities,
multi-core and other HPC techniques and resources.
Moreover, there is a lack of standardized frameworks
(scalability tests, varying core counts, and problem
sizes to show how runtime changes) to evaluate and
compare HPC performance in ship routing
applications for weather, hindering systematic
improvement in the field. Limited discussion exists on
how HPC resources should scale to accommodate
increasing complexity in multi-objective optimization
scenarios. The literature inadequately addresses how
existing ship weather routing can overcome time
constraints in dynamic weather routing scenarios
requiring near real-time decision-making.
Additionally, many existing methods for ship weather
routing, such as pathfinding algorithms, depend on a
step-by-step process that creates links between each
step, making it difficult to run them at the same time.
Thus, there is a clear need to assess ship weather-
routing algorithms for implementation on high-
performance computing platforms. This assessment
must identify which steps can run in parallel, such as
route evaluation, weather-data interpolation, and ship-
performance prediction. The existing ship weather
routing models face scalability limitations when
handling global-scale routing problems with high-
resolution environmental data and multiple
optimization objectives. Furthermore, challenges in
managing HPC resources in shared environments are
evident but not thoroughly addressed in the literature.
The computational challenges of processing large
volumes of dynamic environmental data in HPC
environments remain under-explored, and current
ship-routing algorithms exhibit limited parallelization.
The inherent sequential nature of many route
planning algorithms rely on sequential pathfinding
routines that enforce dependencies between steps and
hinder concurrent execution. Although some processes
could run in parallel, some of the core ship weather
routing algorithmic structure demands sequential
processing, weakening the benefits of multi-core
systems.
Recently, Machine Learning (ML) empowered ship
weather routing has shown promise due to the
emergence of ML and its role in various domains;
however, there is insufficient research on effectively
integrating these approaches with HPC for ship
routing applications. ML models potentially address
some of the inherent limitations of traditional
optimization algorithms, but the computational
demands of training and using complex models create
additional challenges for HPC integration.
101
To address the existing challenges, we aim to
outline a proposal based on implementation of HPC in
the existing MOO-SWR algorithm.
7 THE PROPOSED APPROACH TOWARDS SHIP
WEATHER ROUTING WITH MOO AND HPC
Despite major improvements, current methods still do
not have a single system that can manage both engine-
driven vessels and detailed grid models, particularly
when the algorithm’s execution time is limited. To
address the existing challenges, we aim to propose a
solution based on the implementation of HPC in the
existing MOO-SWR algorithm.
The proposed approach begins with a review of
recent literature and practical case studies to identify
algorithms with strong potential for HPC
implementations. Simultaneously, we’ll analyze
existing MOO-SWR methods to pinpoint components
suitable for HPC techniquessuch as parallelization
across processors, shared-memory architectures, or
specialized hardwareand then design and execute
benchmarks to measure CPU/GPU utilization, memory
usage, solution quality, and speedup relative to
sequential baselines on identical hardware/software
stacks.
Next, we’ll evaluate these algorithms across various
HPC software frameworks and hardware
configurations, comparing their performance to
sequential implementations. For example, to maximize
efficiency, we may adopt a hybrid CPUGPU
architecture: GPUs will handle parallel tasks (e.g.,
environmental data interpolation and route-cost
evaluation), while CPUs manage sequential route-
optimization steps, thereby accelerating multi-
objective optimization and containing computational
growth.
The proposed solution will also explore integrating
an ML-based forecasting modeltrained on historical
and real-time datato predict short-term sea and
weather conditions. These predictions will trigger
targeted re-optimizations within the routing
algorithm, avoiding full re-computations when
changes are localized. Together, these measures
enhance speed, accuracy, and resilience in ship
weather routing.
Figure 3 presents part of the proposed solution,
which ensures the ability to provide reliable route
recommendations promptly. According to Figure 3, the
framework gathers dynamic weather data, ship
parameters, and business information. It maps the
ship’s speed and course according to time, cost, and
CO₂ objectives. The framework then selects an MOO-
SWR algorithm based on its capability to operate on
HPC systems. Using the selected algorithm, it chooses
processes that support HPC featuressuch as parallel
processingand specifies the hardware and software
methods for the chosen solver. These methods may
include genetic algorithms or distributed gradient
searches, chosen at runtime based on solution quality.
Finally, it compares solver performance, route
efficiency, and resource use to determine the optimal
HPC approach, routes, and metrics.
Figure 3. MOO-SWR-based HPC
8 CONCLUSIONS
Ship weather routing has emerged as a critical area of
research and application in maritime transportation,
with significant potential to improve safety, reduce
fuel consumption, and minimize environmental
impacts. The multi-objective-based HPC approach
builds on the importance of weather-aware voyage
planning and provides the computational muscle and
methodological sophistication needed to turn routing
into practice in real-world scenarios.
Accordingly, using High Performance Computing
(HPC) for Multi-Objective Optimization (MOO) in
weather-based ship routing is a major advance over
simple, deterministic methods. Deterministic methods
are fast and easy to use. In contrast, multi-objective
methods balance multiple goals in complex, changing
sea conditions. The pure MOO methods face
challenges such as a multidimensional solution space,
trade-off analysis, detailed environmental models,
safety integration, high compute needs, data fusion
and management, real-time updates, and strict
verification and validation. Conversely, the HPC-
supported MOO methods promise to solve the
complex optimization problems and open
opportunities for addressing these challenges with cost
savings and more flexibility.
HPC may use different strategies and resources to
achieve significantly more computing power than a
standard computer. It relies on a combination of
specialized hardware and software to enable complex,
data-intensive applications to run at high speeds and
allow for increased efficiency. HPC involves
aggregating resources, such as supercomputers,
clusters of computers, and others, to enable the parallel
processing of complex calculations across numerous
processors. Moreover, it offers a suite of strategies that
utilize computational power for various tasks. These
strategies can involve expanding computing resources;
for instance by using parallel processing with MPI and
OpenMP to distribute calculations across many cores.
GPU acceleration with CUDA enables dealing with
ensemble simulations for diverse weather and routing
scenarios. Task-based frameworks and hybrid CPU
GPU architectures handle real-time adaptation and
data fusion. These approaches help explore trade-offs,
validate solutions, and meet the demands of complex
routing. However, there is no guarantee from a
theoretical perspective that selecting the HPC strategy
from various optionssuch as MPI-based parallelism,
102
GPU acceleration, cloud-native clusters, or hybrid
modelswill yield the best result. Thus, the selection
of suitable techniques requires analyzing and
comparing these approaches. That analysis demands
profound domain knowledge and strong
programming skills. Despite these hurdles, multi-
objective HPC methods can minimize fuel use, boost
safety, and lower the environmental impact. With
greater HPC availability and increasing computational
demands for algorithms, these methods might gain
broader use in weather-based ship routing. The MOO-
SWR drive for efficiency, safety, and sustainability will
spur further innovation in weather-based routing.
Addressing these challenges and exploring diverse
HPC strategies, researchers can build efficient models
based on HPC to resolve difficult trade-offs in high-
demand computational domains, such as maritime
domains, in general and particularly for ship weather
routing.
Future work will include searching for a validated
unified HPC-based multi-objective ship routing
framework that better matches real-world scenarios,
enabling quick responses under actual conditions. The
primary objective is to develop a highly efficient and
adaptable ship weather routing method capable of
handling large-scale environmental datasets and
rapidly updating route plans in response to changing
conditions, thereby ensuring reliable performance in
time-critical, real-time scenarios.
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