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1 INTRODUCTION

1.1 Rationale

There is a need for a comprehensive systematic review

on OWSCs, despite the increasing research in this area.

This review should:

− Identify the primary supply chain challenges in

offshore wind projects

− Evaluate the use of DSSs through DES

− Identify research gaps and propose future

directions

Offshore wind (OW) has transitioned from

demonstration projects to industrial scale, introducing

a significantly higher level of complexity to its supply

chains in installation, operations and maintenance

(O&M), and end-of-life activities. These chains are

Challenges in the Supply Chain Management Process

for Offshore Wind Farms. A Scoping Review of Decision

Support Tools Utilizing Discrete Event Simulation

M. Rybowski

Gdynia Maritime University, Gdynia, Poland

ABSTRACT: Offshore wind energy (OWE) has become a key component of the global transition toward

renewable energy; however, its supply chains remain highly complex due to harsh marine conditions, weather

dependency, logistical constraints, and high capital intensity. In this context, decision support systems (DSSs)

based on discrete event simulation (DES) are increasingly applied to improve planning and operational efficiency.

This study aims to systematically identify offshore wind supply chain (OWSC) challenges addressed in the

literature, evaluate the application of DES-based DSS, assess the methodological quality of existing studies, and

highlight research gaps and future directions. A PRISMA-guided scoping review was conducted using a

predefined protocol, covering English-language journal, conference, and technical publications from 2010 to 2025.

Following database searches, deduplication, and screening, 30 studies were included from an initial set of 712

records. The results show that DES is widely adopted, with 63% of studies using pure DES and 37% employing

hybrid simulation–optimization approaches; 67% of studies included case-based validation. Seven major

categories of challenges were identified: weather and metocean conditions, vessel and fleet management,

installation processes, port and logistics operations, operations and maintenance, information and coordination,

and cost/time optimization. Reported benefits of DES-based DSS include improvements in cost efficiency, time

performance, system availability, and resource utilization. The findings confirm that DES constitutes a robust and

effective foundation for decision support in offshore wind logistics, particularly under uncertainty and resource

constraints, while hybrid approaches further enhance its capabilities. Nevertheless, significant gaps remain,

including inconsistent modeling assumptions (especially regarding metocean workability), limited transparency

in verification and validation processes, and insufficient coverage of emerging areas such as floating wind,

decommissioning, and digital integration (e.g., IoT, AI, and digital twins). These findings underline the need for

improved standardization, reporting practices, and benchmark datasets in future research.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 20

Number 1

March 2026

DOI: 10.12716/1001.20.01.20

188

distinctly influenced by stochastic metocean

conditions, limited weather windows, vessel

shortages, port congestion, and inter-project resource

conflicts, which exacerbate uncertainty in scheduling,

costs, and availability. The evidence base has expanded

significantly; however, findings continue to be

disjointed across various phases, geographical regions,

modeling traditions, and performance metrics. A

scoping review adhering to PRISMA-ScR guidelines is

necessary to systematically map the diverse literature,

elucidate the scope of existing studies, and furnish

stakeholders — including developers, port authorities,

vessel owners, and policymakers — with a cohesive

understanding of decision-relevant knowledge (Tricco

et al., 2018).

In this body of work, DSSs based on DES have

become a significant method for modeling queuing,

resource contention, and event-driven operations in

uncertain environments. DES is commonly employed

to evaluate “what-if” scenarios regarding fleet

compositions (wind turbine installation vessels –

WTIVs, service operation vessels/crew transfer vessels

– SOVs/CTVs), installation sequences, dispatch

regulations, inventory strategies for essential spares,

and port layouts, frequently considering downstream

techno-economic consequences (e.g., balance of system

expenses, energy not supplied, marginal impacts on

levelized cost of energy – LCOE). Research studies

exhibit significant variation in their modeling

granularity, including aspects such as weather

workability rules, probability distributions, and

replication design, as well as in validation practices

and the transparency of inputs and outputs. A

structured assessment is required to identify the

primary decision classes addressed by DES-based

DSSs, summarize common key performance indicators

(KPIs), and highlight effective practices and limitations

in verification and validation.

A PRISMA-ScR synthesis identifies significant

research gaps and future directions, such as the need

for standardized reporting in metocean workability

and stochastic inputs. Additional priorities include the

establishment of shared benchmark datasets and

replication seeds, the integration of DES with

optimization, agent-based modeling, and digital twins,

explicit coupling to cost, reliability, and environmental

indicators. There is also a need for improved coverage

of emerging contexts such as floating offshore wind

and multi-project fleet sharing. These contributions

will systematically organize existing literature into a

practical taxonomy; identify areas of robust versus

provisional evidence, and outline a strategic agenda for

enhancing DSSs with DES in OWSC management.

1.2 Objectives

The primary objectives of this systematic review are to:

1. Systematically identify and synthesize literature on

OWSC challenges

2. Evaluate the application and effectiveness of DSSs

using DES

3. Assess the methodological quality of included

studies

4. Provide recommendations for future research and

practice

2 METHODS

2.1 Protocol and Registration

This systematic scoping review was performed in

accordance with the 2020 guidelines of the Preferred

Reporting Items for Systematic Reviews and Meta-

Analyses (PRISMA) (Page et al., 2021). The review

protocol was established before the searches

commenced and encompassed predetermined

inclusion/exclusion criteria, search strategies, and data

extraction procedures.

2.2 Eligibility Criteria

2.2.1 Inclusion Criteria

The review employed stringent criteria for the

selection of source materials. Only research

concentrating on OWE, particularly regarding supply

chain management within this sector, was included.

The problems and challenges related to logistics and

transportation during the installation and operational

phases of the wind farm were evaluated. The

publications considered in the study were required to

focus on DSS related to OW and employ the DES

technique. Included were peer-reviewed journal

articles, conference proceedings, or technical reports

published between 2010 and 2025 and written in

English.

2.2.2 Exclusion Criteria

Studies were excluded if they concentrated solely

on onshore wind energy (unless comparative) or other

renewable energy sources without an OW context.

Given the significance of supply chain aspects, there

are issues over the exclusion of research that

concentrates exclusively on energy generation, as well

as purely theoretical models lacking validation or

actual implementation. I also rejected abstracts without

full text availability due to the inability to ascertain the

study's utility. The same rationale warrants the

dismissal of non-English publications lacking credible

translations.

2.3 Information Sources

A thorough literature search was performed utilizing

reputable academic databases: Web of Science,

Emerald, Scopus, ScienceDirect, Wiley Online Library,

and Google Scholar. The advanced AI-driven platform

SciSpace, which provides access to 280 million

publications, was utilized for the search, selection, and

verification process. All inquiries were executed in

November 2025.

2.4 Search Strategy

A comprehensive search strategy was designed

utilizing four principal topic groupings accompanied

by designated keywords:

1. GROUP 1: Offshore Wind, KEYWORDS: offshore

wind, wind farm, marine wind, offshore wind

energy

2. GROUP 2: Supply Chain, KEYWORDS: supply

chain, logistics, transportation, installation,

maintenance, vessel scheduling, operations

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3. GROUP 3: Decision Support, KEYWORDS: decision

support, decision making, optimization,

optimisation, planning, scheduling, DSS

4. GROUP 4: Simulation, KEYWORDS: discrete event

simulation, DES, simulation modeling, simulation-

based optimization

In the 'Offshore Wind' group, the author employed

synonyms and elaborated on the basic concept with

various phrases like: 'marine', 'energy,' and 'farm'. For

the 'Supply Chain' group, supplementary clarifying

terminology pertaining to processes and phases within

supply chains was employed as keywords. The

preliminary search validated the notion that much

study concentrates on particular elements and aspects

of the supply chain. Nonetheless, refrain from using

the phrase ‘supply chain’ in the text of the writing. In

the 'Decision Support' group, the synonym 'decision

making', the acronym 'DSS', and the names of activities

resulting in the decision-making process were

incorporated. In scientific literature, writers frequently

refrain from characterizing their study outcomes as a

decision-making system, necessitating an expansion of

the search parameters to encompass the terms

'optimization', 'planning', and 'scheduling'. The final

group, 'Simulation,' was delineated broadly due to the

frequent application of diverse modeling and

simulation techniques by researchers, as well as the

combination of several simulation and optimization

methods inside a single research project, as

corroborated by initial web searches. This review of

literature focuses on discrete simulation; however,

during the preliminary search phase, its scope was

considerably expanded to include related topics.

Here is an example of a search string used in

SciSpace:

"What are the challenges in managing offshore

wind supply chains using decision support systems

and discrete event simulation?"

And a second example of a search string from

Google Scholar:

"offshore wind" AND "supply chain" OR logistics

AND simulation OR optimization OR "decision

support" OR "decision making" AND "discrete event"

All search strategies were adapted to the specific

syntax requirements of each database.

2.5 Selection Process

The selection procedure adhered to these steps.

Initially, all records obtained from database searches

were loaded into SciSpace, after which duplicate

entries were discovered and eliminated using

automated methods. The remaining papers were

prioritized based on their relevance to the study

question. Two reviewers separately evaluated titles

and abstracts according to established criteria. Full

texts of possibly qualifying studies were obtained and

evaluated.

Papers that merely referenced keywords in the

literature review or bibliography were removed,

despite perhaps satisfying the criteria. The author

specifically eliminated research projects lacking the

discrete simulation employed in the main body of the

paper.

Books, diploma theses, and the majority of

industrial reports were eliminated, despite fulfilling

the criteria and possessing exceptional value.

Numerous papers were collaborative efforts presented

at scientific conferences and published in academic

journals. Some were republished with minimal

alteration and without disclosure of the duplication of

the work in another format. An exception was granted

for two interconnected publications in which the

authors explicitly demonstrated the ongoing nature of

the research and that substantial progress was evident.

2.6 Data Collection Process

Data extraction was conducted methodically utilizing

a standardized form that recorded study identity

(authors, year, title, publication type) and study

parameters (objectives, geographic focus, study

design). The subsequent data derived from

investigations encompass supply chain challenges,

DSS methods, and approaches. The subsequent

extraction pertains to DES specifics, including utilized

software, scale, and validation. The most recent data

collected comprise key findings and results, along with

limitations and future study recommendations.

3 RESULTS

3.1 Study Selection

The PRISMA flow diagram (Figure 1) illustrates the

study selection process in which 712 records were

identified. After removing duplicates, 180 papers

remained, all of which were verified against the

established criteria. At this stage, 150 results were

excluded, ultimately leading to 30 studies being

subjected to in-depth analysis in this review.

Figure 1. The PRISMA flow diagram

3.2 Study Characteristics

3.2.1 Publication Timeline

The 30 included studies were published between

2012 and 2025, with the distribution as follows:

− 2012-2014: 3 studies

− 2015-2017: 10 studies

− 2018-2020: 4 studies

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− 2021-2023: 7 studies

− 2024-2025: 6 studies

Based on this data, it is possible to point to a

particularly high interest of researchers in the subject

matter between 2015 and 2017 and a further

intensification of research since 2022. This is due to the

expansion of research into novel fields associated with

OWE (floating farms, decommissioning) and the

integration of commonly used methods with

contemporary solutions (genetic algorithms, Markov

chains, cascade simulation) into hybrid decision

support models.

3.2.2 Geographic Focus

A significant number of the works lacked

geographical references, and while some incorporated

real-life situations, the data sources were unspecified.

One of the studies was a multi-regional analysis

incorporating data from the United States and Europe,

while another study referenced Asia, namely South

Korea.

− Europe (North Sea, Celtic Sea): 15 studies

− North America & Asia (US & South Korea): 2

studies (one each)

− General/Multi-regional: 15 studies (one multi)

3.2.3 Study Types

In nearly two-thirds of the research investigations,

pure DES was employed; however, in the remaining

studies, it was also hybridized with optimization.

Twenty research projects incorporated case studies

derived from empirical data. The authors endeavored

to integrate several modeling, simulation, and

optimization techniques across 14 research projects.

− Simulation studies (pure DES): 19 studies (63%)

− Hybrid simulation-optimization: 11 studies (37%)

− Case studies with real-world application: 20 studies

(67%)

− Multi-method approaches: 14 studies (47%)

3.3 Supply Chain Challenges Identified

The author identified seven primary categories of

supply chain challenges. Table 1 presents references to

the identified supply chain challenges with particular

research studies.

Table 1. Categories of supply chain challenges

Challenge

category

Paper references

Weather-

Challenges

(Ait-Alla et al., 2016; Bae & Ko, 2023; Barlow et al.,

2015, 2017, 2018; Beinke et al., 2017, 2020; Dalgic et

al., 2015; Dighe et al., 2024; Endrerud et al., 2014;

Halvorsen-Weare et al., 2021; Li et al., 2024; Mancini

et al., 2024; Mohammad et al., 2025; Muhabie et al.,

2015, 2018; Oelker et al., 2017; Rippel et al., 2022;

Smith et al., 2023; Sperstad et al., 2014; Tjaberings et

al., 2022; Vieira & Djurdjanovic, 2024; Vis & Ursavas,

2016; Wu et al., 2024)

Vessel and

Fleet

Management

(Ait-Alla et al., 2016; Bae & Ko, 2023; Barlow et al.,

2015, 2017, 2018; Beinke et al., 2017, 2020; Dalgic et

al., 2015; Dighe et al., 2024; Endrerud et al., 2014;

Halvorsen-Weare et al., 2021; Li et al., 2024; Mancini

et al., 2024; Mohammad et al., 2025; Muhabie et al.,

2018; Rippel et al., 2022; Tjaberings et al., 2022; Vieira

& Djurdjanovic, 2024; Vis & Ursavas, 2016)

Installation

Challenges

(Ait-Alla et al., 2016; Bae & Ko, 2023; Barlow et al.,

2015, 2017, 2018; Beinke et al., 2017, 2020; Halvorsen-

Weare et al., 2021; Lamas-Rodríguez, Taracido‐

López, et al., 2021; Lamas-Rodríguez, Tutor-Roca, et

al., 2021; Lange et al., 2012; Muhabie et al., 2015,

2018; Rippel et al., 2022; Smith et al., 2023; Tjaberings

et al., 2022; Vis & Ursavas, 2016; Wu et al., 2024)

Port and

Logistics

(Ait-Alla et al., 2016; Beinke et al., 2017, 2020;

Halvorsen-Weare et al., 2021; Lange et al., 2012;

Mancini et al., 2024; Mohammad et al., 2025; Rippel

et al., 2022)

Maintenance

and

Operations

(Dalgic et al., 2015; Dighe et al., 2024; Endrerud et al.,

2014; Li et al., 2024; Mostajeran et al., 2017; Oelker et

al., 2017; Sperstad et al., 2014; Vieira & Djurdjanovic,

2024)

Information

and

Coordination

(Ait-Alla et al., 2016; Beinke et al., 2020)

Cost and Time

Optimization

(Bae & Ko, 2023; Barlow et al., 2015, 2018; Chas-

Álvarez et al., 2018; Dalgic et al., 2015; Dighe et al.,

2024; Halvorsen-Weare et al., 2021; Lamas-

Rodríguez et al., 2017; Lamas-Rodríguez, Taracido‐

López, et al., 2021; Lamas-Rodríguez, Tutor-Roca, et

al., 2021; Li et al., 2024; Mancini et al., 2024;

Mohammad et al., 2025; Mostajeran et al., 2017;

Muhabie et al., 2015, 2018; Rippel et al., 2022;

Tjaberings et al., 2022; Vieira & Djurdjanovic, 2024;

Vis & Ursavas, 2016; Wu et al., 2024)

Source: own studies

3.3.1 Weather-Related Challenges

The challenges encountered within this group

pertained to the volatility of meteorological and

oceanographic (metocean) conditions as a pivotal

factor affecting offshore operations. Weather

conditions substantially restrict operational windows

during the installation and maintenance of offshore

wind farms (OWFs). The seasonality of phenomena

must be considered alongside the significance at the

micro scale (values of parameters such as wave height

and wind speed), as it influences work planning and

scheduling. Particular periods of the year present a

restricted number of workable weather windows; tasks

during these intervals may be unfeasible, interrupted,

or considerably prolonged. Weather conditions

influence the accessibility of equipment (vessels) and

the safety of navigation and ongoing operations.

3.3.2 Vessel and Fleet Management

The most common challenges for fleet management

include ship scheduling, deployment optimization,

and routing. Due to the high specialization of vessels

and their restricted availability (limited resources),

decisions about selecting the optimal mix of ships used

and choosing their types (fleet composition) become

important. Significant decisions regarding the fleet

concern the scope and method of chartering vessels, as

well as the optimal selection and means of transporting

crews performing work at sea.

3.3.3 Installation Challenges

During the installation phase of the OWF, the

sequencing and coordination of assembly tasks are

planned. The most significant challenges pertain to the

efficient transportation of large components from their

production facilities and ports to the installation

location. At this stage, it is imperative to minimize time

and costs, considering the risk and uncertainty

management inherent in installation operations. In

such a case, understanding process bottlenecks and

mitigating or removing these constraints is essential.

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3.3.4 Port and Logistics

Maritime operations in the OW industry involve

port logistics, transportation, warehousing, and

inventory management. In this context, effective

management of storage and assembly areas (pre-

assembly) in ports is essential, along with the

coordination of component deliveries. Additionally, it

is important to maintain the proper balance between

storage expenses and inventory accessibility while

optimizing loads and handling processes in ports.

3.3.5 Maintenance and Operations

During the phase of operation of the OWFs, the

most efficient system maintenance strategy is selected,

frequently integrating various types based on the

specific service activity or component. The restricted

availability of human resources (technicians) and

assets requires their appropriate allocation. Another

thing to consider is the accessibility of turbines under

severe metocean conditions. The ultimate outcome of

these activities should be a decrease in operational

expenses.

3.3.6 Information and Coordination

The pivotal element for supply chain efficiency is

the information system, which, when properly

structured, ought to enhance the coordination of

information transmission among its links. The main

challenges pertain to delays in sharing information and

the visibility of resources and flow items within the

supply chain. High-quality information constitutes the

cornerstone of decision-making systems.

3.3.7 Cost and Time Optimization

The final set of challenges refers to establishing a

foundation for decision-making and optimization

approaches inside OWSCs. The primary issues relate to

reducing the portion of expenses associated with the

operational phases of the OWF and total project

expenditures. Most activities in this domain are time-

sensitive, necessitating meticulous planning and

synchronization of tasks with the project timeline

(schedule optimization). The selection of several

alternative time and cost strategies, along with

resource combinations, presents a challenge for cost-

benefit analysis. Elevated risk levels in the OW

industry require trade-offs between the risk and the

expenses associated with its mitigation.

3.4 Decision Support Systems and Discrete Event

Simulation

3.4.1 Simulation Approaches

All 30 included studies employed DES as a core

method.

Nineteen research projects employing pure DES

approaches concentrated on modeling installation or

maintenance processes, evaluating logistical decisions

and resource allocation, while also assessing weather

impacts and operational constraints.

Muhabie's research focuses on enhancing the

installation process of offshore wind farms by applying

discrete event simulation. The primary goal is to

simulate the entire transport, assembly, and

installation of wind turbine components at sea, aiming

to improve planning and reduce risks and

uncertainties in OWF installations (Muhabie et al.,

2015). The same author in the following paper chose

the core methodology as a discrete-event simulation

model designed to identify favorable installation

strategies for offshore wind farms. Manufacturing

constraints, such as the need for cranes or the

sequential assembly of components, are considered as

they affect each assembly process. The simulation

integrates metocean models to account for the

significant impact of weather uncertainties on OWF

installation (Muhabie et al., 2018). The study prepared

by Vis and Ursavas developed a simulation-based

decision-support tool to analyze the effects of different

logistics approaches, especially considering weather

disturbances during the installation phase. This tool

helps project planners evaluate various logistical

concepts, compare alternatives, and estimate total

durations for operations, aiding in schedule and

budget development (Vis & Ursavas, 2016). A discrete

event simulation model was used in the study of Ait-

Alta to investigate the impact of different information

sharing scenarios on the installation process. The

model considers parameters like the number of wind

turbines, vessels, components, and planning periods

(Ait-Alla et al., 2016). The same topic was developed by

authors in the next paper, which investigates the

impact of information sharing on the installation

processes of offshore wind farms through process

modeling and simulation-based analysis. It highlights

the challenges in OWF installation due to dynamic

environments and the critical role of communication

for cost-efficient logistics. The study investigated the

influence of execution time-related performance

measures, including average time in manufacturer

storage, average time in port, total installation time,

and average vessel usage time (Beinke et al., 2020). The

work of Tjaberings develops a decision support tool

using discrete-event simulation to evaluate different

strategies for installing offshore wind turbine

substructures. The primary goal is to compare these

strategies in terms of time and cost, particularly for

monopile and jacket substructures, which are the most

common types (Tjaberings et al., 2022).

The results generated by Dalgic in his approach

assist in decision-making related to O&M and logistics

strategies, allowing for the identification of favorable

solutions that offer the highest financial and

operational benefits. The created methodology aims to

optimize O&M costs, minimize revenue loss, and

maximize power production. It achieves its objectives

by simulating various factors, including climate

parameters, different failure modes, and the use of

various transportation systems such as helicopters,

Crew Transfer Vessels (CTVs), Offshore Access Vessels

(OAVs), and jack-up vessels (Dalgic et al., 2015).

Studies of Mostajeran introduce a heuristic

optimization and simulation-based decision support

system designed to improve the operation and

maintenance scheduling for offshore wind farms. The

system aims to address the complex resource allocation

problems inherent in O&M tasks, which are often still

managed manually in real-world scenarios. The

generated plans are analyzed using stochastic, event-

discrete simulation to identify the best-performing

options and filter out less effective ones (Mostajeran et

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al., 2017). Lamas-Rodríguez & Tutor-Roca demonstrate

in their study the utility of discrete-event simulation as

a robust tool for risk management in complex

manufacturing environments, particularly for offshore

wind projects with overlapping timelines. By

simulating various scenarios and quantifying risks, it

provides a data-driven approach to optimize project

scheduling, cost, and overall profitability (Lamas-

Rodríguez, Tutor-Roca, et al., 2021). Recently Dighe

introduced an integrated methodology to evaluate

operations and maintenance costs for floating offshore

wind turbines, specifically by incorporating vessel

motion dynamics into simulations. The methodology

combines UWiSE (Unified Windfarm Simulation

Environment), a discrete-event simulation tool, with

SafeTrans, a voyage simulation software (Dighe et al.,

2024).

The core of the methodology in the Barlow study is

a discrete-event simulation tool used to assess the

duration of OWF installation under various scenarios

and to explore the impact of varying operational

capabilities of specific installation tasks. Monte Carlo

simulation is utilized to generate multiple realizations

of synthetic weather time series, each statistically

representative of historical data from the OWF site

(Barlow et al., 2015). In the later study by the same

author, methodology centers on a detailed discrete-

event simulation, underpinned by robust synthetic

weather modeling and Monte Carlo analysis, to

provide a comprehensive and accurate assessment of

OWF installation logistics, costs, and durations under

uncertain conditions. The core methodology involves a

multi-threaded discrete-event simulation model

designed to represent the offshore wind farm

installation process. Each installation vessel, supply

barge, and support operation is represented by a

separate thread, which models a unique sequence of

operations (Barlow et al., 2017). In the subsequent

author’s paper, the simulation model employs a

synthetic weather time-series model to provide a

realistic estimation of how installation operations will

progress. That model uses a correlated autoregression

approach to generate synthetic hourly weather series

based on statistical analysis of hindcast (historical)

weather data, incorporating significant wave height

and wind speed (Barlow et al., 2018). In his study,

Smith successfully demonstrates a methodology using

DES combined with Monte Carlo and time series

analysis to estimate the durations and underlying

distributions of floating offshore wind turbine. This

approach provides valuable insights for project

planners, enabling them to understand the impact of

resource availability, seasonal weather, and location on

project timelines and to design out project risks (Smith

et al., 2023). In recent work, Mancini introduces the

platform module, which leverages UWiSE's existing

frontend and backend functionalities to incorporate

activities, resources, and weather dependencies into

discrete event simulations. This approach allows for

the consideration of offshore operation complexities,

such as weather and resource dependencies, which are

often heavily approximated or not included in holistic

cost models (Mancini et al., 2024).

Eleven studies explored hybrid simulation-

optimization, integrating DES with mathematical

optimization techniques such as mixed-integer linear

programming (MILP) and genetic algorithms. These

studies utilized simulation for evaluation and

optimization for decision-making, incorporating

Monte Carlo simulation for uncertainty analysis.

In 2017, Lamas-Rodríguez introduced an

innovative parametric tool designed to quantify project

risks in manufacturing, specifically tailored for wind

turbine foundation projects, using DES. The tool's

primary purpose is to identify and quantify risks

associated with supply chain delays and to propose

and evaluate mitigation plans (Lamas-Rodríguez et al.,

2017). The following year, the same author group

prepared a paper that addresses the complex task of

identifying, quantifying, and mitigating risks and

opportunities in manufacturing projects, specifically

within the context of offshore wind processes. It

proposes a methodology that combines discrete event

simulation and an optimizer based on genetic

algorithms (GAs) to achieve these goals. GAs are used

to design new manufacturing strategies or plant

layouts by searching for the best combination of

simulation model parameters that meet project

objectives (Chas-Álvarez et al., 2018). In his study,

Halvorsen-Weare runs an underlying module that

integrates optimization and simulation to solve

installation scenario problems; it uses optimization

with a genetic algorithm, while the simulation

procedure is an agent-based discrete event simulation.

The underlying optimization problem is bi-objective,

focusing on minimizing both total installation time and

total installation cost (Halvorsen-Weare et al., 2021).

The authors of the next study propose an innovative

approach that integrates discrete event simulation for

investment analysis, specifically for offshore wind

manufacturing processes. An investment analysis

algorithm was devised and implemented within the

model to calculate NPV, IRR, and payback period

(Lamas-Rodríguez, Taracido‐López, et al., 2021). The

framework created by Barlow integrates discrete-event

simulation and robust optimization, leveraging their

respective strengths. The simulation component helps

estimate the cost and duration based on user-defined

asset selections, while the optimization component

provides an installation schedule robust to weather

uncertainties (Barlow et al., 2018).

The core of the study methodology by Rippel is a

cascading discrete-event simulation framework. This

framework is designed to support operations by

determining suitable resupply cycles for components

and adapting them to the current and predicted needs

at the base port. The cascading simulation framework

is combined with offline mathematical optimizations.

This combination is crucial for deciding demand-

driven and suitable resupply cycles from a pool of

routes (Rippel et al., 2022). The hybrid modeling

framework presented by Bae & Ko in their paper offers

a robust solution for planning the installation of

offshore wind farms. By integrating discrete event

simulation, mixed-integer linear programming, and

Markov Chain Monte Carlo methods, it effectively

addresses the challenges posed by stochastic weather

conditions and complex logistics, ultimately aiming to

minimize project lead time and improve decision-

making for OWF developers (Bae & Ko, 2023). In the

next paper, researchers introduce a model that

integrates nested genetic algorithms with discrete-

event simulations to optimize maintenance scenarios

influenced by operational and environmental

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parameters. The model aims to optimize spare-part

logistics and maintenance decisions across

geographically distributed offshore wind turbines,

considering stochastic part failures, logistical

complexities, and environmental uncertainties like

weather conditions (Vieira & Djurdjanovic, 2024). The

paper by Li & Bijvoet concludes that the developed

discrete event simulation model, coupled with a

simulated annealing algorithm, provides a robust tool

for optimizing vessel fleet chartering decisions for

offshore wind farm maintenance. A discrete event

simulation-based model is proposed to illustrate how a

mixed vessel fleet, comprising crew transfer vessels

(CTVs), field support vessels (FSVs), and heavy lift

vessels (HLVs), conducts maintenance operations for

an offshore wind farm. The optimization objective is to

minimize the total average O&M costs, which include

costs for maintenance tasks, penalties for delayed

completion, vessel-related costs (charter, mobilization,

fuel, technicians), and production losses. By

considering condition-based maintenance and various

uncertainties, the model helps reduce O&M costs and

enhance the overall performance and competitiveness

of the offshore wind sector (Li et al., 2024). In last year's

work of Wu, an offshore wind power resource-

allocation-optimization model is formulated based on

DES. This model simulates the construction process

and monitors the state of wind turbines, personnel, and

meteorological conditions in real time. A

comprehensive strategy optimization index system is

developed, which includes critical indicators to assess

the effectiveness of resource allocation strategies. The

minimum working hours, the window period

utilization rate, the resource-allocation-optimization

rate, and the cost-benefit ratio (Wu et al., 2024) are

some of these indicators.

The paper by Dalgic employs a comprehensive

methodology centered around a time-domain Monte

Carlo simulation approach to optimize operation and

maintenance activities for offshore wind farms. This

approach integrates various factors such as

environmental conditions (wind speed, wave height,

and wave period), operational analysis of

transportation systems, investigation of failures (type

and frequency), and simulation of repairs. This allows

the operator to assess different strategies and compare

them based on generated revenue (Dalgic et al., 2015).

The most comprehensive study by Mohammad

adopted a hybrid analytical approach, combining

several quantitative modeling techniques. Mixed-

Integer Linear Programming was used for emission-

minimizing routing to find optimal routes and

schedules while keeping costs and carbon emissions

low. It also set up multi-echelon supply routes

involving port terminals, floating hubs, and offshore

platforms. DES was employed to evaluate offshore

scheduling performance under variability, model

operations affected by factors like port congestion and

bad weather, and dynamically schedule operations.

Monte Carlo Simulation was used to assess cost and

delivery fluctuations across uncertain operational

scenarios and show how uncertain load changes, fuel

prices, and weather events can affect outcomes. And

finally, Multi-Criteria Decision Analysis was applied

using AHP-TOPSIS to rank alternative marine fuel

types based on multiple conflicting criteria such as

cost, delivery reliability, and emission intensity

(Mohammad et al., 2025).

Fourteen research projects involved multi-method

approaches; specifically, DES combined with agent-

based simulation, some researchers incorporated

Markov Chain Monte Carlo for weather modeling, and

others applied multi-criteria decision analysis (MCDA)

alongside simulation.

Endrerud, in his paper, introduces a novel multi-

method simulation model designed to support

decision-making in the marine logistics of offshore

wind park O&M. By combining agent-based and

discrete event modeling, it effectively captures the

complexity and variability of the system, enabling

developers to make informed choices regarding vessel

configuration, maintenance strategies, and overall

operational efficiency (Endrerud et al., 2014). Oelker

models the maintenance processes of an offshore wind

farm by means of a discrete event and agent-based

simulation model. The objective is to schedule the

maintenance tasks, taking into account all real

restrictions based on historical data in order to

determine important factors and potential operational

improvement. The simulation was used to determine

the optimal number of resources needed to perform

maintenance activities by keeping the resource

utilization at an acceptable level (Oelker et al., 2017).

Another researcher investigates the potential benefits

of resource sharing during the installation phase of

offshore wind farms using a discrete event and agent-

based simulation study. The research highlights the

significant impact of weather restrictions on

installation times and resource utilization and

demonstrates that resource sharing can lead to

substantial savings in the offshore wind energy

installation process. Initial variable assignments are

defined through a Monte Carlo simulation (Beinke et

al., 2017). Halvorsen-Weare describes a sophisticated

tool that integrates a genetic algorithm for generating

optimal installation schedules with an agent-based

discrete event simulator for robust evaluation under

real-world conditions, particularly accounting for

weather uncertainties and component logistics. The

underlying problem is formulated as a bi-objective

optimization task, aiming to minimize both the total

installation time and the total installation cost of

offshore wind farms (Halvorsen-Weare et al., 2021). In

the other study, to incorporate real weather data and

account for weather variability, a Markov Chain Monte

Carlo method is used to generate stochastic weather

instances. This provides an integrated look-ahead

viewpoint for turbine component installation

decisions, enabling robust planning (Bae & Ko, 2023).

In Mohammad's paper, MCDA played a vital role in

providing a holistic framework for evaluating complex

trade-offs in decarbonizing marine logistics, ensuring

that environmental goals were balanced with

operational and economic considerations. This

approach facilitated a thorough evaluation that

transcended single-objective optimization

(Mohammad et al., 2025).

In eight studies, an integrated DSS framework was

proposed as a comprehensive decision support tool,

incorporating different decision layers (strategic,

tactical, and operational) together with interactive

interfaces for decision-makers.

The combined DSS framework created by Barlow

enhances the individual capabilities of both simulation

and optimization models through feedback channels,

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offering a decision-support tool for planning and

bidding stages. It helps OWF developers gain a realistic

understanding of the impact of uncertain weather

conditions and identify appropriate logistical

installation decisions (Barlow et al., 2018). Halvorsen-

Weare created the tool, which features a web-based

user interface that allows users to define installation

scenarios and provides decision support. The

architecture includes a web server, a dynamic web

server (using the Python-based Django framework), a

job queue management server, and a PostgreSQL

database for storing information on vessels, ports,

components, and user-defined scenarios (Halvorsen-

Weare et al., 2021). Intuitive input and output

interfaces were developed by Lamas-Rodríguez to

facilitate model use and result interpretation. The input

interface allows modification of model parameters and

configuration of scenarios, including process

durations, workstation numbers, queue capacities, and

defect rates. The output interface displays annual cash

flow calculations and updates NPV, IRR, and payback

period as the simulation progresses (Lamas-

Rodríguez, Taracido ‐ López, et al., 2021). Mancini

introduces and verifies a new discrete event simulation

tool, UWiSE Decommission, designed for high-fidelity

modeling of offshore wind and solar farm

decommissioning campaigns. The tool allows for

detailed assessment of alternative decommissioning

scenarios, considering complexities like weather and

resource dependencies that are often approximated or

neglected in simpler models. A graphical user interface

(GUI) allows for reviewing inputs, defining method

statements through an interactive process editor using

block diagrams, and tuning simulation settings

(Mancini et al., 2024). The study by Mohammad

successfully integrated optimization models,

simulation techniques, and decision analysis tools to

provide a comprehensive understanding of how vessel

routing, fleet composition, and fuel selection influence

the carbon intensity, cost, and reliability of offshore

supply networks (Mohammad et al., 2025).

3.4.2 Simulation Software and Tools

Studies reported using various simulation

platforms, including

− commercial DES software: AnyLogic (Beinke et al.,

2017, 2020; Endrerud et al., 2014; Oelker et al., 2017;

Rippel et al., 2022), ExtendSim (Chas-Álvarez et al.,

2018; Lamas-Rodríguez et al., 2017)), NOWIcob

(Sperstad et al., 2014), FlexSim (Lamas-Rodríguez,

Taracido ‐ López, et al., 2021; Lamas-Rodríguez,

Tutor-Roca, et al., 2021), Simio (Bae & Ko, 2023;

Tjaberings et al., 2022);

− custom simulation or optimization models (tools)

based on: Python (Halvorsen-Weare et al., 2021; Li,

Jiang, et al., 2024; Vieira & Djurdjanovic, 2024),

MATLAB (Bae & Ko, 2023; Barlow et al., 2017, 2018;

Tjaberings et al., 2022), open-source DESMO-J

(Mostajeran et al., 2017), SimPy (Mohammad et al.,

2025), and Python’s PuLP library (Mohammad et

al., 2025);

− specialized offshore wind tools: UWiSE (Dighe et

al., 2024; Mancini et al., 2024), SafeTrans (Dighe et

al., 2024);

− DEVS (Discrete Event System Specification)

frameworks (Barlow et al., 2018; Dalgic et al., 2015);

− own installation model and simulation tool (Barlow

et al., 2015);

− for some studies there is no specification about

software used (Ait-Alla et al., 2016; Muhabie et al.,

2018; Vis & Ursavas, 2016; Wu et al., 2024).

3.4.3 Validation and Verification

Twenty research projects with case study validation

used models of actual offshore wind installations. In

eight investigations, industry professionals

participated in the evaluation of model assumptions or

the outcomes achieved. Nine research studies utilized

historical data for validation against real project data,

while five studies performed sensitivity analysis.

3.5 Key Findings from Included Studies

3.5.1 Installation Optimization

The study by Barlow highlights that innovative

developments targeting weather-sensitive operations,

particularly loading and installation of wind turbine

generators and jackets, can lead to substantial

reductions in installation duration and costs. These

improvements, especially those related to increasing

operational wind limits, are more impactful than

simply increasing vessel capacity (Barlow et al., 2015).

The simulation tool proposed by the same researcher

demonstrated that logistical decisions significantly

impact the cost and duration of offshore wind farm

installation. A case study examining various logistical

choices revealed that vessel optimization alone can

achieve substantial savings, potentially up to 50%. The

tool helped instill confidence in regulators and

investors by presenting a methodically interrogated

installation plan and a suite of "what-if" scenarios

(Barlow et al., 2017). The next author’s paper proved

that use of the introduced framework has led to

estimated savings of approximately 14% (tens of

millions of GBP) in installation costs for SSE

Renewables, primarily by improving the efficiency of

installation operations for turbine foundations, inter-

array cables, and offshore substation platforms

(Barlow et al., 2018).

The study by Muhabie concludes that offshore wind

farm development is highly sensitive to disturbances in

the logistics chain, particularly those caused by

weather conditions. The use of discrete event

simulation provides a robust framework for planning,

controlling, and assessing risks and uncertainties in

OWF installations, ultimately improving efficiency and

reducing costs (Muhabie et al., 2015). In the following

paper, the author demonstrates that the results

obtained from the simulation approach are valuable for

supporting decision-making related to OWF

installation and logistics strategies. It enables the

assessment of different decisions' consequences,

leading to the selection of favorable solutions that

minimize lead time (Muhabie et al., 2018).

The simulation prepared by Beinke demonstrates

that weather conditions are a critical factor influencing

offshore wind farm installation. However,

implementing a resource-sharing approach can

effectively mitigate these challenges by significantly

reducing resource usage times and improving overall

installation efficiency, thereby offering considerable

economic benefits. When comparing scenarios with

and without resource sharing, it is clear that resource

sharing cuts down on the time that HLVs are used

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across all OWFs by 35.37% (Beinke et al., 2017). The

subsequent paper of Beinke concludes that

comprehensive information sharing, particularly

encompassing weather forecasts, port capacity, and

installation vessel availability, is vital to improving the

planning and control of OWF installation logistics.

While weather forecasts are important, their accuracy

and interdependencies with other shared information

items are key to achieving significant performance

improvements and reducing overall installation time

and costs. The study investigated the influence of

execution time-related performance measures: average

time in manufacturer storage (AVTMS), average time

in port (AVTP), total installation time (OIT), and

average vessel usage time (AVVU) (Beinke et al., 2020).

Vis and Ursavas highlight in their paper that

strategic choices in pre-assembly, vessel loading, and

managing offshore lifts are necessary to optimize the

installation of offshore wind farms, significantly

affecting both project timelines and costs. The inherent

challenges posed by weather and component size

necessitate advanced decision-support tools to

enhance efficiency and economic feasibility in this

growing industry (Vis & Ursavas, 2016). Other authors

demonstrate that through DES, it is possible to identify

an optimal overlapping strategy that significantly

increases profit while maintaining a low and

acceptable level of risk, adhering to crucial project

constraints like delivery timelines. The methodology

provides a robust framework for decision-making in

complex manufacturing environments (Lamas-

Rodríguez, Tutor-Roca, et al., 2021). Tjaberings

concludes that for current market conditions, shuttling

strategies generally outperform feeder strategies.

While strategies with shorter installation times often

incur higher costs, a more holistic market perspective,

considering earlier revenue generation, could shift

preferences toward these faster strategies. The research

highlights the need to align financial interests among

stakeholders and suggests future research into CO₂

emissions as a performance indicator (Tjaberings et al.,

2022).

The results earned by Smith demonstrate that

discrete event simulation combined with time series

analysis effectively reveals how resource allocation,

seasonal weather patterns, and geographical location

significantly influence the duration and potential

delays in assembling floating offshore wind turbines.

Simulations of transit between ports showed

significant intra-annual variations in delay. For

instance, 83% of simulations starting in July

experienced zero delays, whereas only 23% of

simulations starting in January resulted in no delays

(Smith et al., 2023). In their study, Bae and Ko proved

that the hybrid modelling framework successfully

minimizes the time required for installing offshore

wind farms by dynamically integrating simulation and

optimization with stochastic weather data. The study

highlights the critical influence of the number of

turbines, vessel availability, and seasonal weather

patterns on project completion time and efficiency.

(Bae & Ko, 2023). Wu's research simulation results

demonstrated that the model can effectively simulate

the construction process and monitor various states in

real-time. For a specific project, the simulation system

predicted a construction time of 23 days, saving 13

days compared to the original 36-day plan. This led to

a 36% increase in resource-allocation-optimization

(RAOA) efficiency. The calculated cost-benefit ratio

was 0.638, indicating that the actual cost was lower

than the expected cost, leading to good economic

benefits for the project (Wu et al., 2024).

3.5.2 Maintenance Optimization

The simulation model prepared by Endrerud

effectively demonstrated that for the specific offshore

wind park scenario, using two Crew Transfer Vessels

(CTVs) was a more efficient and cost-effective solution

than a single Service Operation Vessel (SOV), leading

to higher availability (97% compared to 93,5%), lower

lost production, and reduced marine logistics costs

(more than 55%) (Endrerud et al., 2014). Dalgic states

that by selecting the most favorable O&M plan, a 3%

improvement in availability and a 24% decrease in total

O&M cost can be expected. The paper demonstrates

that a detailed simulation model can effectively guide

O&M planning for offshore wind farms, highlighting

critical factors such as maintenance timing,

transportation system selection, and cost drivers to

achieve optimal operational and financial outcomes

(Dalgic et al., 2015).

The results of the simulation by Oelker show that

the influence of increasing the number of utilized

resources on OWF availability is limited due to

weather limitations on the deployment of resources.

Furthermore, the implementation results demonstrate

the advantages of condition-based monitoring. The

time between the detection of the failures and their

occurrence also plays an important role in enhancing

the availability of the OWF (Oelker et al., 2017).

Mostajeran states the simulation provides crucial Key

Performance Indicators (KPIs) to aid decision-making,

including the success probability of each task, resource

utilization and costs, generated wind energy (or

opportunity costs for stagnant turbines), and

identification of the critical path during O&M activities

(Mostajeran et al., 2017). Integrated O&M simulation

tools created by Dighe aimed for the self-hoisting crane

strategy (SHC) to be the most cost-effective, reducing

costs by up to 64% while achieving 97–98% availability.

The floating-to-floating (FTF) strategy also showed a

notable reduction, decreasing normalized Maintenance

and Downtime Cost (MDC) by approximately 58%

compared to tow-to-port (T2P) (Dighe et al., 2024).

The study by Vieira and Djurdjanovic demonstrates

the effectiveness of nested genetic algorithms in

optimizing floating offshore wind maintenance. It

offers practical suggestions for how electricity prices

and weather conditions influence short-term tactical

decisions regarding spare parts and scheduling, while

highlighting the long-term strategic importance of

optimizing inspection intervals and component-

specific repair thresholds for financial viability and

operational efficiency (Vieira & Djurdjanovic, 2024). In

their paper, Li presents several key results regarding

the optimal chartering decisions for vessel fleets

supporting offshore wind farm maintenance, focusing

on computational time, optimal fleet configurations,

and cost comparisons. The offshore environment was

identified as the most influential factor for

maintenance implementation. The length of the charter

contract also significantly influenced costs; longer

charter lengths could lead to wasted vessel utilization

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if tasks were completed quickly, while shorter, more

flexible charters could save costs (Li et al., 2024).

3.5.3 Supply Chain Coordination

The study by Ait-Alla demonstrates that

information sharing significantly enhances the

performance of offshore wind farm installation

processes. The combined sharing of weather forecasts

and port capacity information yields the most

favorable outcomes, leading to reduced installation

times and improved resource utilization. A scenario

with port capacity and weather forecast information

sharing showed the best overall performance and led

to the shortest installation time and reductions in port

capacity utilization for various components from 7%

up to 55% (Ait-Alla et al., 2016). Rippel proved that the

cascading simulation and optimization framework

offers a more adaptive and efficient approach to

managing the supply of offshore wind turbine

components. It significantly reduces the required base

port storage capacity and maintains lower inventory

levels, while also providing transparency and

flexibility to respond to unpredictable weather

conditions, ultimately leading to more cost-effective

and reliable installation processes (Rippel et al., 2022).

Two groups of authors concluded that their

integrated supply chain models identified critical

bottlenecks—the handful of wind turbine installation

vessels (WTIV) has become a bottleneck of new

constructions of OWF globally (Bae & Ko, 2023; Beinke

et al., 2020). Other authors state that integrating

Discrete Event Simulation into investment analysis

provides a comprehensive and accurate understanding

of project viability, outperforming traditional

spreadsheet methods by accounting for variability and

enabling optimization. Simulation helped to gain

significant improvements, i.e., the average throughput

of jackets increased by more than 70%, and blockages

in the assembly stations for components were reduced

by 80% (Lamas-Rodríguez, Taracido‐ López, et al.,

2021). Mancini carried out a sensitivity analysis and

demonstrated that the total expected decommissioning

campaign costs can vary by more than 30% depending

on the start date. The most favorable months (April to

June) showed the lowest costs and minimal variance,

showing significant impact of weather delays on the

duration of offshore campaigns (Mancini et al., 2024).

3.5.4 Decarbonization and Sustainability

In a recent study by Mohammad, MILP-based

network flow optimization successfully reduced CO₂

emissions by 22% while ensuring service reliability

across all demand points. This was achieved through

optimizing routing and maintaining operational

efficiency. The Multi-Criteria Decision Analysis

identified bio-LNG and hydrogen propulsion systems

as the optimal choices for marine fuels. This ranking

was based on a comprehensive evaluation of emission

performance, cost-effectiveness, and availability trade-

offs. Hypothesis testing conducted in the study

confirmed significant relationships between the type of

fuel used, the structure of the network, and the overall

emission performance of the logistics operations

(Mohammad et al., 2025).

4 DISCUSSION

4.1 Summary of Evidence

This systematic review aggregated findings from 30

studies regarding challenges in the offshore wind

supply chain and the utilization of DSSs through DES.

The review indicates that DES is extensively utilized

among modelers. Numerous research, including those

reviewed, employed discrete event simulation, thereby

affirming its significance as a tool for analyzing the

offshore wind supply chain. Research examined a

broad spectrum of challenges encompassing

meteorological uncertainty, vessel management,

installation planning, maintenance optimization, and

cost reduction. The integration of DES with

optimization approaches, multi-criteria analysis, and

various simulation techniques has shown improved

decision support capabilities. Sixty-seven percent of

the research used case study validation, indicating

practical significance. Documented enhancements

varied from 14% to 98% across multiple performance

parameters (cost, time, availability, and resource

utilization).

4.2 Key Challenges in Offshore Wind Supply Chains

4.2.1 Weather Dependency

Weather is the fundamental and pervasive

challenge at all phases of the supply chain. In contrast

to onshore operations, offshore activities encounter

restricted operational windows, considerable planning

uncertainties, substantial cost repercussions from

delays, and safety limitations that confine operations.

The primary impact of that difficulty is that it

necessitates the integration of robust weather

forecasting into adaptable planning methods. From

this point of view, it's easier to see how important

simulations are for figuring out what the weather will

be like.

4.2.2 Specialized Vessel Requirements

The OW sector necessitates specialized vessels that

are limited, expensive to charter or own, and

extensively utilized across projects, rendering them

important bottlenecks in supply chains. Vessel fleet

decisions throughout all phases of the OWF supply

chain hold strategic significance, requiring industry-

wide coordination of marine activities and fostering

the potential for shared vessel pools. Investment

decisions require advanced analysis employing

simulation and optimization methods.

4.2.3 Installation Complexity

The installation of offshore wind components

involves sophisticated sequencing and coordination of

numerous interdependent tasks. Extensive logistical

activities in OW supply chains can lead to significant

financial repercussions from errors or delays.

Integrated DSSs are essential instruments for various

scenario analyses, providing significant simulation

value for installation planning. It is crucial for such

support systems to extract advantages and insights

from previously completed projects.

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4.2.4 O&M Cost Burden

Operations and maintenance account for 15-30% of

the total costs incurred by OWFs (Oelker et al., 2017)

and can constitute up to 30% of the LCOE for floating

offshore wind turbines specifically (Dighe et al., 2024).

Moreover, considerable unpredictability in planning

and enduring operational issues present potential for

cost reduction. During the O&M phase of an OWF, a

sophisticated decision-making tool is required,

featuring resource optimization capabilities, predictive

maintenance strategies, and a long-term planning

module.

4.3 Effectiveness of Decision Support Systems

4.3.1 DES as Core Method

Discrete event simulation demonstrated efficacy in

encapsulating the complexity of the OW system by

simulating its interdependencies and restrictions. DES

adeptly manages uncertainty by including stochastic

variables such as weather conditions or system

breakdowns into the simulation model. In a DSS

utilizing the DES, one can compare different strategies

and scenarios to assess alternatives. The DES technique

facilitates the discovery of system bottlenecks and

enhancement possibilities, as well as the quantification

of risks and their effects within the risk assessment

process.

4.3.2 Value of Hybrid Approaches

The integration of DES with other techniques

improved the effectiveness of identifying optimal

solutions in simulated scenarios. The integration of

DES with agent-based or system dynamics approaches

facilitates the incorporation of diverse objectives and

stakeholder preferences. Furthermore, the capabilities

of machine learning and advanced analytics are

employed to examine extensive data sets generated by

OWF systems.

4.3.3 Practical Implementation

Research illustrated practical significance by

employing real-world case studies and validation.

There is evident substantial coordination with industry

and professional participation. The practical outcomes

of the projects are tools, including user-friendly

interfaces and visualizations, complete with scenario

planning modules and what-if analysis functionalities.

Barlow's proposed models have been embraced by

industry partners to guide installation strategy

development; for instance, SSE Renewables employed

the framework for the Beatrice Offshore Wind Farm

installation project, a 600 MW wind farm in the North

Sea (Barlow et al., 2018).

4.4 Research Gaps and Future Directions

4.4.1 Methodological Gaps

The author identified several types of research gaps

in his review. The initial category pertains to

methodological weaknesses. The analyzed studies

included a narrow range of research methodology

reporting; some had limited descriptions of the

methods used. The lack of basic procedural

information makes it impossible for other researchers

to replicate the studies. However, some works

included information about the possibility of obtaining

the necessary data directly from the authors of the

studies. Nevertheless, standardizing guidelines for

reporting research methods would be a tremendous

help to other scientists.

Most studies provided a brief paragraph about the

validation of the implemented model, but the

discourse on this subject was somewhat limited.

Numerous studies are conference presentations,

constrained in scope; however, the following

development of these findings into articles in scientific

journals still lacked adequate information regarding

validation (Ait-Alla et al., 2016; Beinke et al., 2020).

Certain studies provided an in-depth description of the

verification procedures employed in the research

(Tjaberings et al., 2022; Vis & Ursavas, 2016). A more

stringent approach for validating scientific

achievements in this domain appears to be necessary.

Comparable limitations apply to the sensitivity

analysis, which is either absent or constrained in the

majority of scientific studies. The examined papers

exhibit an inadequate analysis of uncertainty

propagation. It is essential to establish rigorous

methods for assessing uncertainty in OW research.

4.4.2 Application Gaps

The second category of highlighted research

deficiencies pertains to the utilization of the DES

methodology and DSS inside contemporary

technology solutions. Limited research has focused on

floating wind farms (Dighe et al., 2024; Smith et al.,

2023; Vieira & Djurdjanovic, 2024). This research

direction is crucial because of the distinct problems this

technology encounters in contrast to fixed-bottom

wind farms. This focus will become significant as this

technology matures.

Likewise, limited research examines the

decommissioning phase (Mancini et al., 2024). The

supply chain's significance will escalate toward the

conclusion of the OWF lifespan as more projects enter

the aging phase. The organization of reverse logistics

necessitates distinct research, as it will become an

important aspect of the supply chain in the offshore

wind energy sector in approximately a decade.

Engaging in a circular economy establishes research

objectives for science related to component recycling

and the development of sustainable supply chain

models for wind turbines.

The offshore wind industry also establishes a

research domain for the application and advancement

of contemporary digital technologies. Limited research

exists that explores the difficulties of integrating IoT

and digital twins, developing real-time decision

support systems, or leveraging the potential of artificial

intelligence and machine learning in OWF

management.

4.4.3 Geographic Gaps

The third group of research gaps was identified as

slight differentiation in studies regarding geographical

scope. There is a lack of sufficient research in the

literature from regions where OW is developing

particularly rapidly (Asia, the Americas). This results

198

in an inability to identify challenges that are typically

regional. It is similarly challenging to locate scientific

papers focused on research concerning extreme

environmental conditions and the potential for

leveraging these findings in the development of OW.

Frontier (pioneering) projects are crucial, as they offer

researchers substantial qualitative data for modeling

and simulating OWF logistics under challenging

conditions.

4.4.4 Integration Gaps

The final set of gaps recognized pertains to

integration issues. Research on strategic planning at

the multi-project level, resource sharing within the

industry, and location and operational decisions

considering the throughput capacity of port

infrastructure is absent in the literature. The

investigated researches present a notable limitation in

their neglect of the influence of policy on OW-related

issues, encompassing regulatory constraints,

incentives, market dynamics, and competitive effects.

4.5 Recommendations

4.5.1 For Researchers

The author provides suggestions for different

stakeholder groups participating in OW and adopting

modeling and simulation-based DSS solutions based

on the identified research gaps.

It is recommended that researchers enhance

methodological rigor by implementing uniform

reporting practices, improving protocols for validating

and verifying results, and, whenever feasible,

providing descriptions of methods, techniques,

models, and complete data. Future research areas

include floating wind farms, decommissioning

logistics, digital technology integration, and

developing circular economy models for OW. It is

advised that industry and academia strengthen their

collaboration to enhance the quality of research

findings. Inter-institutional research, open-source

model development, industry best practices, and data

sharing can all help achieve this. Addressing region-

specific issues and broadening research to include

emerging markets would be important additional

components that would enhance our understanding of

OW. Comparative studies across regions would also be

desirable as an outcome.

4.5.2 For Industry Practitioners

Industry practitioners are advised to use decision

support tools and ensure that planning tools are

simulation-based and connected with optimization

operations. Scenario analysis ought to be employed in

the formulation of strategic decisions. The cornerstone

of a DSS is information; therefore, it is essential to

enhance operational data collection, establish industry

databases, and standardize data formats. For research

reasons, the OW industry should provide anonymized

data, which can serve as a foundation for modeling and

optimization efforts.

The operational proposals for the industry

primarily address the coordination of vessel and

resource sharing, along with collaboration in

infrastructure construction. Establishing uniform

industry standards is becoming essential, as is

enhancing information interchange within supply

chains and the overall supply network.

From an industry development standpoint,

investments in novel solutions are crucial,

encompassing enhanced DSSs, digital twin

technologies, artificial intelligence, and machine

learning. Novel logistics concepts must undergo

testing in several iterations prior to their deployment

utilizing these technologies.

4.5.3 For Policymakers

Policymaker recommendations in the scientific

domain generally encompass sponsoring research to

enhance the OW supply chain, fostering collaboration

between industry and academics, investing in

workforce development, and promoting data-sharing

efforts.

In relation to the operation of the OW industry, the

primary responsibility of legislators should be to

facilitate the construction of shared infrastructure,

standardize rules across various regions, and establish

capabilities for the sharing of vessels and resources.

The OW industry cannot progress without fostering

innovation; thus, it is essential to establish incentives

for the use of sophisticated technology, sustainable

practices, and the concepts of a circular economy.

Testing modern solutions through pilot and

demonstration projects would not have been feasible

without backing at the national and global levels.

4.6 Limitations of This Review

The limitations of this study include restricting the

review to English-language publications only. The

author may have overlooked studies contained in

specialized databases to which he lacked access. A

similar situation occurred with gray literature

generated beyond conventional academic distribution

channels. The author recognizes the utility of this

source type; nonetheless, its dispersion, variability,

and lack of homogeneity considerably restrict its

usefulness. The source search was performed in

November 2025, and undoubtedly, some research

findings from this year are not yet accessible (preprints,

fee-based databases, absence of full texts).

The author recognizes that the review is limited in

synthesis due to the heterogeneity of the studies, which

constrained the potential for quantitative synthesis.

The diverse approaches of researchers in simulation

and the numerous design contexts restricted the

comparison of the study findings.

5 CONCLUSIONS

This systematic review offers a thorough synthesis of

studies about the challenges in the OWSC and the

utilization of DSSs using DES. The analysis indicates

that:

1. DES is a recognized and efficient instrument for

tackling OW supply chain challenges, with

extensive implementation in installation,

maintenance, and logistics contexts.

199

2. Weather uncertainty, vessel management, and cost

optimization are the most significant and

widespread challenges throughout all phases of the

supply chain.

3. Hybrid methodologies that integrate DES with

optimization, multi-criteria analysis, and additional

techniques provide improved decision support

efficacy and practical significance.

4. Substantial performance enhancements (15-50%

across several metrics) can be attained using

simulation-based decision support, as evidenced by

real-world case studies.

5. Methodological quality issues are present, as the

majority of studies lack adequate information for

comprehensive bias evaluation, underscoring the

necessity for enhanced reporting requirements.

6. Research gaps persist in floating offshore wind,

decommissioning, digital technology integration,

and emerging markets.

The findings endorse the ongoing advancement and

utilization of simulation-based DSSs for OWSCs.

Nonetheless, the discipline would gain from:

− Uniform reporting standards for simulation

research

− Improved validation and verification procedures

− Increased emphasis on emerging challenges

(floating wind, decommissioning)

− Incorporation of digital technologies (IoT, AI/ML,

digital twins)

− Broadened geographic scope and cross-regional

studies

With the global expansion of the OW industry,

advanced DSSs will be essential for managing complex

supply chains, minimizing costs, and guaranteeing

project success.

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