Published at : 25 Jan 2024
Volume : IJtech
Vol 15, No 1 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i1.6492
Arif Junaidi | School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang 14300, Malaysia |
Hartono Yudo | Department of Naval Architecture, Engineering Faculty, Diponegoro University, Semarang 50275, Indonesia |
Hasnida Ab-Samat | School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang 14300, Malaysia |
This literature review focuses on research related
to Port State Control (PSC) inspections carried out on board ships, with a
particular focus on areas of interest and data analysis methods adopted during
research. The five steps involved in the literature review process include: (1)
determining the research questions, (2) gathering works of literature for
review, (3) conducting selection and screening based on the inclusion and
exclusion criteria, (4) analyzing the selected works of literature and ensuring
the quality of the data, and (5) reporting the result of the literature review.
Based on the comprehensive searches throughout various databases, the most
high-impacted databases in sequence were Elsevier, Taylor & Francis Online,
(Multidisciplinary Digital Publishing Institute) MDPI, Springer, and others
such as Emerald Insight, Science Direct, and Google Scholar. There were 2,023
articles published between 2018 and 2022 gathered during the initial search
process, and the 41 final papers were ultimately selected for in-depth analysis
after a selection process. The four main research focuses found from this
literature review were (1) the selection of ships for PSC inspection, (2) the New Inspection
Regime (NIR), (3) the identification of
findings during PSC inspection, and (4) detention of ships under PSC
inspection. From the literature review, 37% of the final selected articles
focused on vessel selection, 33% noted the findings, 23% focused on vessel
detention, and only 7% explored the New Inspection Regime (NIR). From the
review, most articles used the Bayesian networks (BN) method for data analysis,
followed by traditional analysis, Ideal Solution Similarity Order Priority
Technique (TOPSIS), Hierarchical Analytical Process (AHP), Apriori Algorithm,
and Gray Relational Analysis (GRA). This result could provide valuable
information to professionals in the maritime industry, and this literature
review signifies the importance of Port State
Control (PSC)
inspections in fostering the better development of the global maritime
transportation system especially to ensure maritime safety.
Detention; Flag state; Maritime safety; Port State Control (PSC); Ship inspection
Maritime transport constitutes a vital component of the broader transportation network and accounts for approximately 90% of all international freight, granting a substantial advantage over other modes of transport due to its cost-effectiveness. Maritime transport is a safer, cheaper, greener, and more energy-efficient than other modes of transport. This mode enables the simultaneous conveyance of large cargo volumes while effectively reducing transportation expenses. It is important to strengthen maritime safety and protect the environment towards a cleaner environment (Chuah et al., 2023). The importance of maritime transportation has further escalated, particularly with the advent and advancements in container transport (Emecen, Kara, and Oksas, 2016). There is growing concern around shipping and expectations in the shipping industry due to the technological revolution. In response to societal pressures, the maritime industry is facing an increasing demand to adhere to more stringent regulations and voluntary standards in the areas of health, safety, security, and the environment. This implies that health, safety, environment, and security expectations have a moderate impact on the future of the shipping industry. In the context of sustainable sea transportation, safety emerges as a critical aspect intricately linked to ship management and operation (Oloruntobi et al., 2023). The ship management organization is responsible for ensuring the safe operation of the ship and, therefore, must develop a robust monitoring system. It is also imperative that the organization ensures compliance with all relevant international conventions and regulations (IMO, 2019a). To enhance maritime transportation safety, the relevant authorities in countries worldwide have established Port State Control (PSC), including ship inspection strategies aimed at evaluating the safety benefits of existing inspection regulations and identifying opportunities for further improvement (Heij, Bijwaard, and Knapp, 2011; Liou et al., 2011). PSC inspections significantly enhance maritime safety in shipping (Knapp, Bijwaard, and Heij, 2011).
Port State Control (PSC) is the inspection of foreign
ships in national ports to verify that the vessel's condition meets the
requirements of international regulations. In that context, PSC is one of the
criteria to determine the effectiveness of the flag State in complying with
ratified international maritime conventions to protect the reputation and
enhance competitiveness to do business in that country. These inspections are conducted by port
states on foreign ships during their port visits to ensure shipping safety as
well as prevent marine pollution. Furthermore, port states are crucial in
supplementing the international conventions and regulations established by flag
states. Conducting a series of PSC inspections reduce the number of
non-compliant ships, ensuring shipping and maritime environmental safety (Chen et al., 2019). PSC is an integral
part of the conventions and codes developed and ratified by the International
Labour Organization (ILO) and International Maritime Organization (IMO), Safety
of Life at Sea (SOLAS), Marine Pollution (MARPOL), as well as Maritime Labour Convention (MLC), 2006,
which specified the scope of PSC inspections for precise items onboard ships.
The relevant instruments forming the basis of PSC inspections typically include
the aforementioned conventions and codes in most MOUs. The Memorandum of
Understanding (MoU) is intended to encourage shipowners to register their ships
under a flag with a low retention rate as the flag State has taken appropriate
measures to ensure compliance with its core obligations. International treaty.
Large shipowners and flag registries, including the registry, recognize the
importance of checking the MOU. The discovery of defects on board a ship due to
non-compliance with IMO conventions may result in the detention of the vessel
until the defects are rectified (Chuah et al., 2021).
In response to a series of oil tanker tragedies in the
1970s, coastal states established ten regional Port State Controls (PSCs) and
signed memorandums of understanding (MoUs) to conduct safety inspections on
foreign-flagged ships upon their arrival at the port (Perepelkin
et al., 2010). Figure S1 in supplementary shows the execution of
nine Memorandums of Understanding for port state control regional agreements (IMO, 2019b). Intergovernmental
organizations have been given observer status at the IMO for several regional
PSC regimes. Representatives from these regional agreements regularly engage in
IMO meetings and provide the Sub-Committee on Implementation of IMO Instruments
(III Sub-Committee) with thorough reports on their annual activities. This
information is utilized to evaluate their adherence to IMO standards. Port
State Control inspections specifically target vessels that fall below the
required standards and fail to comply with international maritime regulations.
Although these inspections are costly, many do not result in any detentions,
and many inspections do not uncover any deficiencies (Cariou
and Wolff, 2015).
This research aimed to conduct a
literature review on previous research related to port state control
inspections on board ships to gain insight into the trends of focus and data
analysis methods in PSC inspection in recent years. The review used a five-step
approach which started by formulating research questions. The second step was
to search for literature throughout the various database platform. It was
followed by selecting as well as screening relevant articles. The fourth step
was analyzing and synthesizing qualitative findings, and the last step was
conducting quality control (Perry and Hammond,
2002). The data analysis used a qualitative approach through interactive
models consisting of three stages, namely reduction, display, and conclusion or
verification (Miles, Huberman, and Saldaña, 2014).
During the reduction process, articles were selected based on pre-set inclusion
and exclusion criteria to ensure all the necessary information for the desired
goals. Furthermore, the data were sorted according to the research purpose by
grouping articles based on topics and providing codes for easy identification.
The first step in a literature
review is to establish research questions. This particular review aims to
investigate the port state control inspections on ships global trend. This
literature review's primary focus is to provide insights and answers to the
following research inquiry:
RQ1. What is the
current focus of the research on port state control inspection?
RQ2. What method was
utilized in analyzing the data in the research?
In the second stage, the literature search was conducted by indexing databases and journal publishers. The search for articles used the keyword port state control inspection entered into the search field of various high-impact databases such as Taylor & Francis Online, Emerald Insight, Science Direct, and Google Scholar, as well as Springer Link. The search was limited to articles published between 2018 and 2022. From the initial search, there were 2,023 journal articles retrieved. The third step was to screen the duplicate articles until they were reduced to 600 articles. It was further screened based on the research topic and filtered according to the criteria of relevance to the study's objectives, findings, and results, with a focus on articles containing relevant keywords. From 600 articles, there were 129 articles selected for full-text screening. Following the inclusion and exclusion criteria determined before the screening, an in-depth review based on research focus, unit of analysis, context, and quality assessment produced 41 articles fulfilling the requirement for analysis in this literature review. During the analysis of the 41 articles, the researcher maintains the quality of the data analyzed to ensure the information and the result inferred from the research.
Figure 1 The Article and Publisher Distribution
The journal and publisher for this literature
review were retrieved from the highly impacted database. Most articles were
from Elsevier, followed by Tailor & Francis, MDPI, Springer, and others. To
identify the trends in PSC inspections, the review focused on articles
published between the years 2018 and 2022 to ensure relevance and novelty.
3.1. Ship
selection for PSC Inspections
As port state control (PSC) inspection is
crucial for ensuring safe ship operations, effective inspection planning and
selection are vital, given the limited resources and inspection costs. However,
to tackle this challenge, several approaches have been developed. One such
method involves utilizing a classifier called balanced random forest (BRF) from
the learning library (Yan, Wang, and Peng, 2021a) to set ships for inspection as well as take into
account factors that impact the outcomes. Another approach is developing a
combined model for ship risk prediction by Yan,
Wang, and Peng (2021b), which considers ship deficiencies as well
as detentions and employs a distinct methodology for ship selection. In the
study conducted by Wang, Yan, and Qu (2019), a data-driven Bayesian network classifier known
as the Tree Augmented Naive (TAN) Bayes classifier was employed to choose 250
ships for PSC inspection. The selection was made using information culled from
Hong Kong and Tokyo's MoU databases, covering the period from January to July
2017, which yielded an average of 130% more deficiencies detected compared to
the previous method. The same model was formulated by Yang et al. (2018), incorporating a strategic game framework in
empirical research, which provided insights and suggestions for port
authorities. Similarly, Liu et al. (2022) utilized a Bayesian Network (BN) to assess
detention risk and identify related factors, providing insights on ship
selection priorities for ship owners and port authorities. Yan et al. (2022a) created two data-driven frameworks for
predicting ship risk. These frameworks incorporated features from the existing
ship selection scheme and utilized Shapley additive explanations to generate
individual ship predictions, effectively addressing pertinent concerns in the
field. Such as concern regarding the quality of ship maneuvering that is
particularly important from a technical and operational point of view. The
maneuverability of ships is very interesting by ship owners, operators,
seaports and state management agencies. In addition to collision avoidance,
ship maneuverability should be prioritized due to its importance and impact on
ship safety and operability (Sunarsih et al., 2023).
Another research focusing on container
ships with inspection data in Taiwan from 2015 to 2018 utilized Statistical Process
Control (SPC) to monitor and select PSC inspections due to a lack of effective
tools for inspection and monitoring. SPC was reported to be useful in detecting
time-dependent abnormal instances and monitoring maritime inspections (Yuan et al., 2020). Additionally, a
framework was developed employing the Analytical Hierarchy Process (AHP) to
assess the priority of 14 factors and four subfactors that influence PSC
operation and selection. This was done by collecting data through
questionnaires and face-to-face interviews with 53 experts from Taiwan,
including PSC officers at Ports of Keelung and Taipei in October 2019 (Yuan, Chiu, and Cai, 2020). Another analysis
method involved using the Apriori Algorithm to produce rules for PSC inspection
selection by analyzing two sets of data, one with 8,089 PSC inspections and the
other recorded for five years from 2015 to 2019. This was retrieved from the
online Tokyo MoU database. The research findings indicated a significant
correlation between the number of detentions and the deficiencies discovered
during inspections. Specifically, these frameworks were designed to address
concerns related to ships registered under blacklisted countries (Osman et al., 2020). A similar data mining
method was applied to historical PSC inspection records, and the research
identified rules that can help inspectors effectively (Chung
et al., 2020). Another research focused on ship accidents and
their impact on safety levels, using the Bayesian Network (BN) to investigate
the PSC inspection effect (Fan, Zheng, and Luo,
2022).
Given the limited resources and high
costs associated with PSC inspections, improving their efficiency is paramount.
However, to this end, Yan, Wang, and Fagerholt
(2021) proposed mathematical optimization models that coordinate
inspection resources across multiple ports, while Yan,
Wang, and Peng (2021b) discussed the advantages and disadvantages of
using different ports to improve ship selection efficiency in MoUs. Another
approach was taken by Chuah et al. (2022),
who identified the risk profile of the ship and designed the inspection area,
proposing two coordinated inspection strategies for liner as well as tramp ship
types. (Yan, Wang, and Fagerholt, 2021). An
entropy-based Grey Relevance Analysis methodology was employed to analyze ship
inspection trends between 2017 and 2020 using COVID-19 pandemic outbreak data (Akyurek and Bolat, 2020).
3.2. New Inspection Regime (NIR)
After thorough
preparations, the New Inspection Regime (NIR), introduced on January 1, 2011,
aimed to enhance the Port State Control (PSC) inspection system effectiveness.
According to the official report of the Paris MoU, the NIR was considered the
most significant and transformative change in the PSC system in recent years.
It brought about substantial improvements compared to the previous system,
which had been in place for 30 years based on an agreement. This modification
was required to bring the Paris MoU into compliance with shifting maritime
patterns worldwide, new IMO instruments, and a more impartial approach to ship
targeting and inspection. The NIR's primary goal was to promote high-quality
shipping while tightening controls and imposing sanctions on ships with poor
maintenance. NIR marks a notable departure from the previous regime's 25
percent inspection commitment and six-month inspection intervals, which placed
undue strain on the maritime industry and PSC authorities. The research on its implementation
was conducted using data from the Paris MoU and Bayesian Network models. A
macroscopic comparative analysis was conducted to identify the areas where the
NIR enhances the PSC inspection system, vessel quality, as well as maritime
safety (Yang, Yang, anda Teixeira,
2020).
Another research
identified additional parameters like the ship's age, type, deadweight, number
of deficiencies, and other factors such as port State, flag State performance,
and recognized organization. The study utilized data from the Tokyo MoU to conduct
a binary logistic regression analysis, which aimed at examining detention
decisions. Additionally, a multi-factor decision-making analysis was performed
using the same dataset (Xiao et al., 2020).
According to recent literature, the New Inspection Regime (NIR) has been found
to exhibit greater economic efficiency compared to other types of regimes. To
assess and compare the efficiency of inspection implementation across ten
Memorandums of Understanding (MoUs), a Super-Slacks-Based Measure (super-SBM), as well as the Malmquist Production Index (MPI), were
employed. Three inspection regimes were analyzed in the evaluation process (Xiao et al., 2021). The research of Buana, Yano, and
Shinoda (2022)
showed another alternative for another parameter to consider as all seagoing
ships shall be equipped with adequate ballast water equipment in accordance
with Regulation D-2, Section D, Standards for Ballast Water Management,
International Convention for the Control and Management of Ships’ Ballast Water
and Sediments, promoted by the International Maritime Organization. Although it
is difficult to choose the right equipment since the methods used to develop
the device have similar advantages and disadvantages. To tackle this challenge,
this research proposes an evaluation methodology for outfitting appropriate Ballast Water Management System (BWMS)
equipment by applying multi-criteria analysis combined with the value
engineering concept.
3.3. Evidence
on inspections and deficiencies
PSC inspections aim
to detect significant vessel deficiencies and reduce the likelihood of vessel
casualties. Analyzing the findings on deficiencies realized during PSC
inspections is crucial to reducing or preventing future detentions of ships.
Various research has been conducted to analyze this data. A Bayesian Network
(BN) was employed in a particular study to develop a PSC risk probabilistic
model. This model emphasized the interdependencies and dependencies among the
various risk factors that impact PSC inspections (Wang
et al., 2021). Another research used the same model to predict
detention probabilities contingent on the risk factors influencing PSC
inspection findings in bulk carrier vessels (Yang,
Yang, and Yin, 2018). The Bayesian network model has also been used to examine the various
factors' impacts on ship accidents and simplify the PSC inspection procedure by
identifying key deficiency items (Fan et al.,
2019). Since deficiencies are a significant factor in ship detentions,
research was conducted using harmonized deficiency codes to determine whether
those identified during PSC inspections can forecast future accident risks. The
outcomes of the study can be utilized by maritime authorities to enhance asset
allocation by considering prediction scenarios associated with vessel traffic
data (Heij and
Knapp, 2018).
Similarly, The Bayesian network method was employed to develop a ship accident
model utilizing 17 deficiency items derived from the Tokyo MoU. The findings
confirmed the three defect level variables' dynamics and their impact on vessel
accidents. Additionally, the study revealed the defect level improvement for
ships detained during the initial inspection. (Fan et
al., 2022).
Various methods were
utilized to analyze findings and deficiencies during port state control
inspections. Graziano et al. (2018) employed econometric
analysis to account for differences in observable vessel characteristics
between countries. Osman et al. (2021) used
the Entropy Weight Method (EWM) as well as Grey Relational Analysis (GRA) model
combination to examine PSC inspection findings between 2015 and 2019 from Tokyo
MoU in five selected ports in Malaysia. Yan et
al. (2021) constructed an XG Boost model that predicts ship
deficiency numbers using ship generic, dynamic, and inspection history
characteristics. The model was developed using data from the Tokyo MoU Port
State Control (PSC) regime. Shen et al.
(2021) employed fuzzy importance-performance analysis (F-IPA) and TOPSIS
to examine data on ship deficiencies taken from the Tokyo MoU database. Graziano, Mejia, and
Schröder-Hinrichs (2018) conducted a traditional analysis of PSC reports from
March 2012 to April 2016, designed as a methodical process for assessing and
evaluating printed and electronic documents. Fotteler, Andrioti
Bygvraa, and Jensen (2020) extracted and analyzed deficiencies related to living and working
conditions and certificates and documents from 2010 to 2017, finding an
increasing focus on inspections conducted in European countries. Yan et al.
(2022b)
explored the COVID-19 influence on PSC inspections. They analyzed the
deficiencies per inspection and detention rate average number to assess the
impact.
3.4. Evidence
on inspections and deficiencies
The maritime port
authority conducts port state control inspections to target vessels not
complying with international maritime regulations. Meanwhile, quantile
regressions for count data were utilized to improve the vessel selection
efficiency to evaluate the likelihood of having a high number of specific type
deficiencies, and factors influencing the likelihood of vessel detention were
identified. This method was found to be effective in identifying substandard
vessels (Cariou and
Wolff, 2015). In
the Indian Ocean MoU, the age of the vessel (40%), recognized organization
(31%), and place of inspection (17%) were identified as the main contributors
to vessel detention (Cariou, Mejia, and Wolff, 2009). A comprehensive
analysis was conducted on 32,206 Port State Control (PSC) inspections within
the Paris MoU region, spanning from January 1, 2014, to December 31, 2015, and
reported that the number and background of PSCOs on board and the inspection
country could impact inspection outcomes. Econometric analysis was utilized to
manage the differences in observable characteristics of vessels inspected in
different countries (Graziano et al., 2018).
From the summary in
Figure 2, this literature review found that the trends from the selected
articles were mostly focused on the PSC topics related to the inspection (37%),
followed by topics of finding (33%), then topics about detention (23%), and the
last discuss NIR (7%). While the trends of the analysis method used in PSC from
these selected articles are composed of Bayesian Network (BN) (25%),
Traditional analysis (15%), followed by Apriori Algorithm (10%), then TOPSIS,
Mathematic & Logit Model, Grey Relational Analysis (GRA), and
Computer-based Analysis, each mounted to 7%.
Various models have
been developed to predict ship deficiencies during port state control
inspections, with the aim of targeting substandard vessels. One such model is a
ship deficiency prediction model that was developed using 1,974 initial Port
State Control (PSC) inspection data at the Hong Kong port, employing the
Tree-Augmented Naive Bayes (TAN) classifier. The data used for
this model covered the period from January 2016 to December 2018. The study
evaluated existing ship selection methods employed in various ports and put
forward novel approaches for ship selection (Yan,
Wang, and Peng, 2021b). Yan et al.
(2021) used the same data to develop a state-of-the-art XGBoost model
that considers ship generic, dynamic, and inspection historical factors to
predict ship deficiency numbers accurately. This model incorporates domain
knowledge specific to the shipping industry, including ship flags, recognized
organizations, and company performance. The Apriori Algorithm was used in
another research (Osman et al., 2020) to
produce valuable rules. A comprehensive model for predicting ship risk was
developed and validated, taking into account both deficiencies and detention
records (Yan, Wang, and Peng, 2021b). Ship
selection before PSC inspection is not solely based on identifying substandard
vessels but also takes into account other factors such as age, flag, recognized
organization, and ship manager (Cariou, Mejia, and
Wolff, 2009).
Figure 2 Summary of focus area and data analysis methods
3.5. Ship
Detention Under Port State Control (PSC) Inspection
As previously discussed, the detention
of a ship can significantly impact the maritime industry, particularly for ship
managers or operators, due to cargo delivery delays. Consequently, several
research studies have been conducted to identify the causes of ship detentions.
One such investigation examined the deficiencies number, and the detention
probability was analyzed using Port State Control (PSC) inspection data from
the Indian Ocean Memorandum of Understanding (MoU) over a span of five years.
The results indicated that vessel age, recognized organizations, and inspection
location contributed to detention, but ship flag and type were not included (Cariou, Mejia, and Wolff, 2009).
Preliminary research has employed various methods to assess ship detention and
PSC efficiency, including the Bayesian network model, examining different
deficiencies and factors influencing ship accidents (Fan
et al., 2019). Another research focused on bulk carrier vessels
and developed a risk assessment model regarding related factors that influence
PSC inspections. The data used in this research were recorded from PSC
inspection results from the Paris MoU between 2005 and 2008, and a new way to
predict detention probabilities was also identified. The results did not offer
any method or solution to reduce ship detention (Yang,
Yang, and Yin, 2018). One
research that could provide guidance to ship owners or managers was based on a
strategic management approach. The data used were recorded from 6,374
deficiencies found on 2,653 detained ships in the Black Sea Region detention
list from 2005 to 2006 and were analyzed using the fault tree method (Akpinar and Sahin,
2019). Information from the Tokyo MOU region’s PSC detention database from
2000 to 2016 was also evaluated, and each detention factor was subjected to
additional pre-processing to identify regularity deficiencies during PSC
detention. The results of the big data analysis, which used association rule
techniques, provided countermeasures that ship management could serve as a
reference (Tsou, 2019). In another study,
PSC regime data from various periods were utilized to conduct an empirical
analysis using the Grey Rational Analysis
(GRA) model with enhanced entropy weight. The study's goal was to offer
suggestions for putting effective procedures into place in ship safety
inspections carried out by port states (Chen et
al., 2019).
It is important to analyze PSC detention
data from different PSC regimes to understand detention records globally. In
the Black Sea Region (BS MoU), a computational analysis method was used to
analyze 29,954 PSC data inspections from 2012 to 2017. The objective of this
analysis is to identify the key factors that should be given primary
consideration when selecting foreign vessels for inspection by Port State
Control (PSC) authorities (Sanher, 2020).
The Bayesian Network (BN) approach, widely used for data analysis, was integrated with
the TOPSIS to analyze a novel methodology for controlling ship detention risk
in PSC inspections. (Yang et al., 2021).
The Bayesian network approach was also used to conduct risk assessments in new
building construction in shipyards (Basuki et
al., 2014). A different approach was proposed by Akyurek and Bolat (2021), who used the Analytical
Hierarchy Process (AHP) approach to evaluate PSC detention ranking based on
Paris Memoranda of Understanding (MOU) data inspection records from 15
countries in the EU. In other industries, the Analytic Hierarchy Process (AHP)
approach was used for supplier evaluation and selection, combined with the
Delphi Method (Al Hazza et al., 2022). According to the results, the Safety of
Life at Sea (SOLAS) and Fire Safety Systems (FSS) were identified as the top
priorities for regulation in almost all countries. Research by Ravelo-Mendivelso et al. (2023) that was
based on the AHP (Analytic Hierarchy Process) multi-criteria method identified
the best alternatives to improve the thermal efficiency of the housing and the
shell heat exchanger under actual operating conditions. A key motivation for conducting
research based on identified needs, in partnership with oil, natural gas, and
alternative energy industries, is to analyze and understand the key criteria
that directly impact the thermal performance of heat exchangers. Emecen Kara (2022) employed the TOPSIS to assess
flag states' performance in terms of marine safety based on PSC inspections.
Each flag state's performance was evaluated in light of its detention rates and
any issues associated with its PSC regimes. The results showed that just around
half of the flag states performed at acceptable levels overall. To harmonize
PSC regimes, TOPSIS was proposed as the uniform method for evaluating flag
state performance. Furthermore, TOPSIS was utilized to assess the major port's
connectivity and the competitiveness of container types in Southeast Asia (Nguyen and Woo,
2021).
The detention of ships can
result in financial losses and harm the reputation of a company. However, to
identify the root causes of detentions of different vessel types,
investigations should include an analysis of the links between deficiencies and
their impact on detentions. Chen et al.
(2022) used Association Rules and PSC inspection data from 2014 to 2020
to identify critical deficiencies leading to vessel detentions. They suggested
improving its safety, reducing environmental pollution, and minimizing shipping
line losses. PSC was established to ensure vessel safety and prevent marine
pollution through inspections of ships, equipment, crew, and operations for
compliance with international conventions. Suhrab et
al. (2022) applied association rule mining techniques in extensive
data analysis to provide countermeasures and references for ship management to
reduce or prevent its detention during PSC inspection. Various data analysis
methods have been proposed to address the significant impact of ship detention
on the maritime industry. There is limited use of combined methods for more
useful results, such as the AHP-TOPSIS approach, which has not been applied to
PSC detention data analysis.
Table 1 Author focus on A Literature Review
Author |
Publisher |
Method |
Objective |
Research Focus | |||
1 |
2 |
3 |
4 | ||||
Yang et al., 2018 |
Elsevier |
Bayesian network (BN) |
Relationship between port authorities and ship
owner, used Paris MoU database |
x |
|
|
|
Graziano, Mejia, and Schröder-Hinrichs, 2018 |
Elsevier |
Computer-based thematic analysis |
Implementation of the Directive 2009/16/EC, used
the Paris MoU database |
|
|
x |
|
Yang, Yang, and
Yin, 2018 |
Elsevier |
Bayesian network (BN) |
The impact of NIR, used Paris MoU database |
|
|
x |
|
Graziano, et al., 2018 |
Elsevier |
An econometric analysis |
Correlation of inspection outcomes each PSC, used
Paris MoU database |
|
|
x |
|
Heij and
Knapp, 2018 |
Tailor & Francis |
Index and logit model |
Prediction of (PSC) inspections on accident, data
from IHS Markit |
|
|
x |
|
Wang, Yan, and Qu, 2019 |
Elsevier |
Bayesian network (BN) |
Identify substandard ships, used Tokyo MoU
database |
x |
|
|
|
Chen et al., 2019 |
Elsevier |
Grey relational analysis (GRA) |
Identify ship detention’s factor, used Tokyo MoU
database |
|
|
|
x |
Akpinar and
Sahin, 2019 |
Emerald |
Fault tree analysis |
PSC approach, used Black Sea MoU database |
|
|
|
x |
Tsou, 2019 |
Tailor & Francis |
Apriori algorithm |
Response of ship management on PSC, used Tokyo
MoU database |
|
|
|
x |
Fan et al., 2019 |
Sage |
Bayesian network (BN) |
The efficiency of PSC, used Tokyo MoU database |
|
|
x |
|
Bai & Wang, 2019 |
Tailor & Francis |
Traditional analysis |
Polar Code on fishing vessels, used Paris MoU
database |
x |
|
|
|
Chung et al., 2020 |
Tailor & Francis |
Apriori algorithm |
Association rule of PSC, Taiwan’s PSC database |
x |
|
|
|
Yang, Yang, anda Teixeira, 2020 |
Elsevier |
Bayesian network (BN) |
Comparation of NIR, used Paris MoU database |
|
x |
|
|
Yuan et al., 2020 |
MDPI |
Statistical process control (SPC) |
Monitoring on PSC, used Taiwan’s PSC database |
x |
|
|
|
Yuan, Chiu, and Cai, 2020 |
MDPI |
The analytical hierarchy process (AHP) |
Independent of PSC regime, used questionary data
from expert |
x |
|
|
|
Osman et al., 2020 |
Tailor & Francis |
Apriori algorithm |
Association rule of PSC, used Tokyo MoU database |
x |
|
|
|
Xiao et al., 2020 |
Elsevier |
A binary logit model |
The effectiveness of NIR, used Tokyo MoU database |
|
x |
|
|
Sanher, 2020 |
Elsevier |
The computational analysis |
Selection proses on PSC, used Black Sea MoU
database |
|
|
|
x |
Akyurek and
Bolat, 2020 |
Springer |
Entropy-based grey relevance |
PSC on pandemic outbreak, used Paris MoU database |
x |
|
|
|
Fotteler, Andrioti Bygvraa, and Jensen 2020 |
Springer |
Traditional analysis |
Impact of MLC ratification, used 7 MoU’s database |
|
|
x |
|
Fan, Zheng, and Luo, 2022 |
Tailor & Francis |
Bayesian network (BN) |
Effect of PSC inspection on ship accident, used
Tokyo MoU database |
x |
|
|
|
Yan, Wang, and Fagerholt, 2021 |
Tailor & Francis |
Mathematical optimization models |
Identify PSC inspection strategy, used PSC data
in mainland China |
x |
|
|
|
Yan, Wang, and
Peng, 2021b |
Tailor & Francis |
Tree-augmented naive Bayes (TAN) |
The impact of NIR, used Paris MoU database |
x |
|
|
|
Xiao et al., 2021 |
Elsevier |
Apply super-SBM and MPI |
Efficiency of PSC regimes, used 8 PSC regime
database |
|
x |
|
|
Yan et al., 2021 |
Elsevier |
XGBoost model |
Optimization in PSC, used Tokyo MoU database |
|
|
x |
|
Akyurek and
Bolat, 2021 |
Springer |
The analytical hierarchy process (AHP) |
Professional Judgement on PSC, used Paris MoU database |
|
|
|
x |
Wang et al., 2021 |
Elsevier |
Bayesian network (BN) |
The risk factors influence on PSC, used Tokyo MoU
database |
|
|
x |
|
Yan, Wang, and
Peng, 2021a |
Elsevier |
Balanced random forest (BRF) |
Factors influencing the PSC result, used Hong
Kong PSC database |
x |
|
|
|
Osman et al., 2021 |
Elsevier |
Grey relational analysis (GRA) |
Identify PSC in Malaysian ports, used PSC
database at Malaysia port |
|
|
x |
|
Yang et al., 2021 |
Elsevier |
Bayesian network (BN) + TOPSIS |
PSC inspection scenarios, used Paris and Tokyo
MoU database |
|
|
|
x |
Shen et al., 2021 |
MDPI |
F-IPA and TOPSIS |
Identify hidden risk of target ship, used Tokyo
MoU database |
|
|
x |
|
Yan et al., 2022 |
Elsevier |
Shapley additive explanations (SHAP) |
Efficiency on ship selection, used 9 PSC MoU
database |
x |
|
|
|
Chuah et al., 2022 |
Elsevier |
Traditional review/analysis |
Safety assessment using PSC data, used PSC
inspection data in Malaysia |
x |
|
|
|
Emecen Kara, 2022 |
Elsevier |
Similarity to an Ideal Solution (TOPSIS) |
Performance of flag states, used Paris and Tokyo
MoU database |
|
|
|
x |
Liu et al., 2022 |
Elsevier |
Bayesian network (BN) |
Identify new risk factors on PSC, used Paris MoU
database |
x |
|
|
|
Fan, et al., 2022 |
Elsevier |
Bayesian network (BN) |
Identify efficiency on PSC, used Paris MoU
database |
|
|
x |
|
Yan et al., 2022b |
Elsevier |
Traditional review/analysis |
Influence of the COVID-19 on PSC, used 8 PSC MoU
database |
|
|
x |
|
Chen et al., 2022 |
Elsevier |
CARMA algorithm |
Identify PSC findings based on association rules,
used Paris MoU database |
|
|
|
x |
Fan et al., 2022 |
Tailor & Francis |
A difference-in-differences (DID) model |
Impact of Sulphur Emission Control Areas on PSC,
used Tokyo MoU, Indian MoU, and Paris MoU database |
|
|
x |
|
Suhrab et al., 2022 |
Specialists Ugdymas |
Computerized Information System |
The potential regularity of ship detention, used
the Tokyo MoU database |
|
|
|
x |
Chuah et al., 2023 |
Elsevier |
Bayesian network (BN) |
Influence factors of PSC on the environment, used
Tokyo MoU database |
|
|
|
x |
Research focus: (1) Ship inspection selection, (2) NIR, (3) Finding and deficiency, (4) Ship detention
According to Chuah et al. (2023), a Bayesian network
model was developed to analyze the factors influencing testing leading to
detention, viz. The flag State, ship type, recognized organization, inspection
authority, and ship age. The flag country has the greatest influence, followed
by vessel type, accredited organization, inspection agency, and vessel age in
descending order of importance. These findings would guide PSC officers and
ship owners to identify critical areas for improving maritime safety, promoting
environmental sustainability, and achieving a cleaner environment.
This literature
review aimed to confirm the trends of the main focus regarding port state
control (PSC) inspection and identify the methods used to analyze the resulting
data and generate outcomes. The adopted methodology consisted of a traditional
review of the existing literature, which was then collectively analyzed to
generate statistical results. The statistical findings indicated that, over the
past five years, research publications had primarily focused on four key
issues, namely ship selection for PSC inspection, the discovery of deficiencies
during the inspection, implementation of the new inspection regime (NIR) for
PSC, and ship detention under PSC inspection. Ship selection was the most
frequently researched area. The second most commonly researched issue focuses
on identifying deficiencies during PSC inspection. However, less research has
examined the implementation of NIR for PSC and highlighted related issues. Ship
detention, which can impact multiple stakeholders beyond the manager, was
another critical issue. Finally, substandard vessels, specifically those with
inadequate maintenance, were identified as a potential area of concern that
could lead to ship detention. Yet, the research on this topic is limited.
Sea
transportation is one of the safe alternatives to travel and has an important
role in the world economy. Ensuring its safety is crucial for both the global
economy and the well-being of passengers and crews. Port State Control (PSC)
inspections are to ensure that foreign ships entering their ports comply with
international safety, security, and environmental regulations, as well as to
offer suitable living and working conditions for their crews. The last line of
defense against subpar shipment is sometimes cited as PSC inspections, which
act as the international law's razor-sharp teeth. PSC inspections must adhere
strictly to maritime laws and Memorandum of Understanding (MOU) guidelines
dictating areas of the ship that needs to be examined more closely. In some
cases, MOUs may launch a concentrated inspection campaign (CIC) to address
specific issues or newly enacted laws. These agreements are crucial in
establishing a consistent and rigorous approach to port state control
inspections worldwide. The majority of the research relied on Bayesian
Network (BN) to analyse the collected data across all the focus areas,
including ship inspection selection, new inspection regime, deficiency during
the inspection, and ship detention under PSC inspection. However, other
analytical methods were also utilized, including Analytical Hierarchy Process
(AHP), Apriori Algorithm, Technique for Order Preference by Similarity to an
Ideal Solution (TOPSIS), binary logit model, Grey Relational Analysis (GRA),
and traditional analysis methods. Some research also employed a combination of
these methods to obtain precise data analysis results. There are no documented
cases where the Analytical Hierarchy Process (AHP) and the Technique for Order
Preference by Similarity to an Ideal Solution (TOPSIS) combination has been
utilized for data analysis, particularly in the context of Port State Control
(PSC) inspection results. This method is particularly useful in achieving
accurate results during data analysis and providing meaningful recommendations
to stakeholders, especially ship management companies involved in shipping
operations, to reduce or eliminate instances of ship detention during PSC
inspection. Following the findings of this literature review, the direction for
future research could explore more about the other focus area of the PSC
inspection e.g. key factor of PSC detention and method to reduce it, which
might be linked to the various changes such as the advanced development of
technology and information and the combination of the data analysis method used
in PSC according to the different situation and environment that might rise in
the future.
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Akpinar, H., Sahin, B., 2019. Strategic
Management Approach for Port State
Control: The Black Sea
Memorandum of Understanding Detention
Analysis. Maritime
Business Review, Volume5(3),
pp. 281–293
Akyurek, E., Bolat, P., 2020. Port State Control
at European Union under Pandemic
Outbreak. European Transport Research
Review, Volume 12(1), pp. 1–13
Akyurek, E., Bolat, P., 2021. Ranking Port State
Control Detention Remarks: Professional Judgement and Spatial Overview.
European Transport Research Review, Volume 13(1), pp. 1–19
Al Hazza,
M.H., Abdelwahed, A., Ali, M.Y., Sidek, A.B.A., 2022. An Integrated Approach
for Supplier Evaluation and Selection using the Delphi Method and Analytic
Hierarchy Process (AHP): A New Framework. International Journal of
Technology, Volume 13(1), pp. 16–25
Bai,
J., & Wang, C., 2019. Enhancing Port State Control in Polar Waters. Ocean
Development and International Law, Volume 50(4),
299–319
Basuki, M., Manfaat, D.,
Nugroho, S., Dinariyana, A.A.B., 2014. Probabilistic Risk Assessment
of the Shipyard Industry using the Bayesian Method. International Journal of Technology,
Volume 5(1), pp. 88–97
Buana, S., Yano, K.,
Shinoda, T., 2022. Design Evaluation
Methodology for Ships’ Outfitting Equipment by Applying Multi-criteria
Analysis: Proper Choices Analysis of Ballast Water Management Systems. International
Journal of Technology, Volume 13(2),
pp. 310–320
Cariou, P., Mejia, M.Q., Wolff, F.C., 2008. On the Effectiveness of Port State
Control Inspections. Transportation Research Part
E: Logistics and Transportation Review, Volume 44(3), pp. 491–503
Cariou, P., Mejia, M.Q., Wolff, F.C., 2009. Evidence on Target Factors
used for Port State Control
Inspections. Marine
Policy, Volume 33(5), pp. 847–859
Cariou, P., Wolff, F.C., 2015. Identifying Substandard Vessels Through
Port State Control Inspections:
A New Methodology for Concentrated Inspection
Campaigns. Marine Policy, Volume 60,
pp. 27–39
Chen, J., Zhang, S., Xu,
L., Wan, Z., Fei, Y., Zheng, T., 2019. Identification of Key Factors
of Ship Detention under Port State Control. Marine
Policy, Volume 102, PP. 21–27
Chen, Y., Lou, N., Liu,
G., Luan, Y., Jiang, H. 2022. Risk Analysis of Ship Detention
Defects Based on Association Rules. Marine Policy, Volume 142, p. 105123
Chuah, L. F., Salleh, N.H.M., Osnin, N.A., Alcaide, J.I., Majid, M.H.A., Abdullah, A.A., Bokhari, A., Jalil, E.E.A, Klemeš,
J.J., 2021. Profiling Malaysian Ship Registration
and Seafarers for Streamlining Future Malaysian Shipping Governance. Australian Journal of Maritime
& Ocean Affairs, Volume 13(4),
pp. 225-261
Chuah, L.F., Mokhtar, K.,
Bakar, A.A., Othman, M.R., Osman, N.H., Bokhari, A., Mubashir, M., Abdullah,
M.A., Hasan, M., 2022. Marine Environment and Maritime Safety Assessment
using the Port State Control Database.
Chemosphere, Volume 304,
p. 135245
Chuah, L.F., Rof’ie, N.R.M., Salleh, N.H.M., Bakar, A.A., Oloruntobi, O., Othman, M.R., Fazlee, U.S.M., Mubashir, M., Asif, S., 2023. Analyzing the Influencing Factors of Port State Control for a Cleaner Environment
via Bayesian Network Model. Cleaner Engineering and Technology,
Volume 14, p. 100636
Chung, W.H., Kao, S.L.,
Chang, C.M., Yuan, C.C., 2020. Association Rule Learning
to Improve Deficiency Inspection in Port State
Control. Maritime Policy
and Management, Volume 47(3),
pp. 332–351
Emecen Kara, E.G., 2022. Determination of Maritime Safety Performance
of Flag States based on the Port State Control Inspections using Technique for Order
Preference by Similarity to an Ideal Solution (TOPSIS).
Marine Policy, Volume 143,
p. 105156
Emecen Kara, E.G., Oksas, O., 2016. A Comparative Analysis of Regional
Agreements on Port State Control. American Scientific Research Journal for
Engineering, Volume 18(1), pp. 259–270
Fan, L., Zhang, M., Yin,
J., Zhang, J., 2022. Impacts of Dynamic Inspection
Records on Port State
Control Efficiency using Bayesian Network Analysis.
Reliability Engineering and System Safety, Volume 228, p. 108753
Fan, L., Zhang, Z., Yin,
J., Wang, X., 2019. The Efficiency Improvement of Port State
Control based on Ship Accident
Bayesian Networks. Proceedings of
the Institution of Mechanical Engineers, Part O: Journal of Risk and
Reliability, Volume 233(1), pp. 71–83
Fan, L., Zheng, L., Luo, M.,
2022. Effectiveness of Port State Control
Inspection using Bayesian Network Modeling.
Maritime Policy and Management, Volume
49(2), pp. 261–278
Fotteler, M.L., Andrioti
Bygvraa, D., Jensen, O.C., 2020. The Impact of the Maritime Labor Convention on Seafarers’ Working and Living Conditions:
An Analysis of Port State
Control Statistics. BioMed Central (BMC) Public Health,
Volume 20(1), pp. 1–9
Graziano, A., Cariou, P.,
Wolff, F.C., Mejia, M.Q., Schröder-Hinrichs,
J.U., 2018. Port State Control
Inspections in the European
Union: Do Inspector’s Number and Background Matter?. Marine
Policy, Volume 88(2),
pp. 230–241
Graziano, A., Mejia, M.Q., Schröder-Hinrichs, J.U., 2018. Achievements and Challenges on the Implementation of the European Directive on
Port State Control. Transport Policy, Volume 72, pp. 97–108
Heij,
C., Bijwaard, G.E., Knapp, S., 2011. Ship
Inspection Strategies: Effects on Maritime Safety and Environmental Protection.
Transportation Research Part D: Transport and Environment, Volume 16(1),
pp. 42–48.
Heij,
C., Knapp, S., 2018. Predictive Power of Inspection Outcomes for Future Shipping
Accidents–an Empirical Appraisal with Special Attention for Human Factor Aspects.
Maritime Policy and Management, Volume 45(5), pp. 604–621.
International
Maritime Organization (IMO). 2019a. Brief History of International Maritime
Organization (IMO). Available Online at: https://www.imo.org/en/About/HistoryOfIMO/Pages/Default.aspx,
Accessed on December 20, 2022
International
Maritime Organization (IMO). 2019b. Port State Control. Available online at:
https://www.imo.org/en/ourwork/msas/pages/portstatecontrol.aspx, Accessed on December
20, 2022
Knapp,
S., Bijwaard, G., Heij, C., 2011. Estimated Incident Cost Savings in Shipping
Due to Inspections. Accident Analysis and Prevention, Volume 43(4), pp. 1532–1539
Liou,
S.T., Liu, C.P., Chang, C.C., Yen, D.C., 2011. Restructuring Taiwan’s Port State
Control Inspection Authority. Government Information Quarterly, Volume 28(1),
pp. 36–46
Liu,
K., Yu, Q., Yang, Z., Wan, C., Yang, Z. 2022. Bayesian Network (BN)-Based Port State
Control Inspection for Paris MoU: New Risk Factors and Probability Training
using Big Data. Reliability Engineering and System Safety, Volume 224, p.
108530
Miles,
M.B., Huberman, A.M., Saldaña, J., 2014.
Qualitative Data Analysis. A Methods Sourcebook. In: SAGE Publication 3rd
Edition
Nguyen,
P.N., Woo, S.H., 2021. Port Connectivity and Competition Among Container Ports
in Southeast Asia based on Social Network Analysis and Technique
for Order Preference by Similarity to an Ideal Solution (TOPSIS). Maritime
Policy and Management, Volume 49(6), pp. 779–796
Oloruntobi,
O., Mokhtar, K., Gohari, A., Asif, S., Chuah, L.F., 2023. Sustainable Transition
Towards Greener and Cleaner Seaborne Shipping Industry: Challenges and Opportunities.
Cleaner Engineering and Technology, Volume 13, p. 100628
Osman,
M.T., Tian, L., Chen, Y., Rahman, N.S.F.A., 2021. Empirical Analysis on Port State
Control Inspection for Foreign-registered Ships in Malaysian Ports. Asian
Journal of Shipping and Logistics, Volume 37(2), pp. 127–139
Osman,
M.T., Yuli, C., Li, T., Senin, S.F., 2020. Association Rule Mining for Identification
of Port State Control Patterns in Malaysian Ports. Maritime Policy and
Management. Volume 48(8), pp. 1082–1095
Perepelkin,
M., Knapp, S., Perepelkin, G., De Pooter, M., 2010. An Improved Methodology to Measure
Flag Performance for the Shipping Industry. Marine Policy, Volume 34(3),
pp. 395–405
Perry,
A., Hammond, N., 2002. Systematic Reviews: The Experiences of a Ph.D. Student. Psychology
Learning & Teaching, Volume 2(1), pp. 32–35
Ravelo-Mendivelso,
K.Y., Villate-Fonseca, M.T., Hernández-Vásquez, J.D., Miranda-Samper, O.M.,
Pacheco-Torres, P.J., Campuzano, M.J., 2023. Thermal and Hydrodynamic
Performance Analysis of a Shell and Tube Heat Exchanger Using the AHP
Multi-criteria Method. International Journal of Technology, Volume 14(3),
pp. 522-535
Sanher, S. 2020. Analysis of Port State Control Inspection Data: The Black Sea Region. Marine
Policy, Volume 112, p. 103757
Shen,
J.H., Liu, C.P., Chang, K.Y., Chen, Y.W., 2021. Ship Deficiency Data of Port State
Control to Identify the Hidden Risks of the Target Ship. Journal of Marine
Science and Engineering, Volume 9(10), p. 1120
Suhrab,
M.I.R., Sayed, M.A.A., Muniandy, T.D., Fuad, A.F.A, Said, M.-H., Ahmed, T., 2022.
Ship Detention Factors Under Port State Control in Malaysia. Baltic Journal
of Special Education, Specialists Ugdymas, Special Education, Volume 1(43),
pp. 8386-8395
Sunarsih,
Jadmiko, E., Zaman, M.B., Malik, A.M.A., Ali, A., 2023. Enhancing A Reliable
Ship Performance Evaluation in Dynamic Maneuvering Conditions–A Gap Analysis. International
Journal of Technology, Volume 14(3), pp. 561-575
Tsou,
M.C., 2019. Big Data Analysis of Port State Control Ship Detention Database. Journal
of Marine Engineering and Technology, Volume 18(3), pp. 113–121
Wang,
S., Yan, R., Qu, X., 2019. Development of a Non-parametric Classifier:
Effective Identification, Algorithm, and Applications in Port State Control for
Maritime Transportation. Transportation Research Part B: Methodological,
Volume 128, pp. 129–157
Wang,
Y., Zhang, F., Yang, Z., Yang, Z., 2021. Incorporation of Deficiency Data Into
the Analysis of the Dependency and Interdependency Among the Risk Factors Influencing
Port State Control Inspection. Reliability Engineering and System Safety,
Volume 206, p. 107277
Xiao,
Y., Qi, G., Jin, M., Yuen, K.F., Chen, Z., Li, K.X., 2021. The Efficiency of
Port State Control Inspection Regimes: A Comparative Study. Transport Policy,
Volume 106, pp. 165–172
Xiao,
Y., Wang, G., Lin, K.C., Qi, G., Li, K.X., 2020. The Effectiveness of the New
Inspection Regime for Port State Control: Application of the Tokyo MoU. Marine
Policy, Volume 115, p. 103857.
Yan,
R., Mo, H., Guo, X., Yang, Y., Wang, S. 2022b. Is Port State Control Influenced
by COVID-19? Evidence from Inspection Data. Transport Policy, Volume 123,
pp. 82–103
Yan,
R., Wang, S., Cao, J., Sun, D., 2021. Shipping Domain Knowledge Informed
Prediction and Optimization in Port State Control. Transportation Research
Part B: Methodological, Volume 149, pp. 52–78
Yan,
R., Wang, S., Fagerholt, K., 2021. Coordinated Approaches for Port State
Control Inspection planning. Maritime Policy & Management, Volume
49(6), pp. 897–912
Yan,
R., Wang, S., Peng, C., 2021a. An Artificial Intelligence Model Considering
Data Imbalance for Ship Selection in Port State Control Based on Detention
Probabilities. Journal of Computational Science, Volume 48, p. 101257.
Yan,
R., Wang, S., Peng, C., 2021b. Ship Selection in Port State Control: Status and
Perspectives. Maritime Policy and Management, Volume 49(4), pp. 600–615
Yan,
R., Wu, S., Jin, Y., Cao, J., Wang, S. 2022a. Efficient and Explainable
Ship Selection Planning in Port State Control. Transportation Research Part
C: Emerging Technologies, Volume 145, p. 103924
Yang,
Z., Wan, C., Yang, Z., Yu, Q., 2021. Using Bayesian Network-based Technique for
Order Preference by Similarity to an Ideal Solution (TOPSIS) to Aid Dynamic Port
State Control Detention Risk Control Decision. Reliability Engineering and
System Safety, Volume 213, p. 107784
Yang,
Z., Yang, Z., Teixeira, A.P., 2020. Comparative Analysis
of the Impact of New Inspection Regime on Port State Control Inspection. Transport
Policy, Volume 92, pp. 65–80
Yang,
Z., Yang, Z., Yin, J., 2018. Realizing Advanced Risk-based Port State Control Inspection
using Data-driven Bayesian Networks. Transportation Research Part A: Policy
and Practice, Volume 110, pp. 38–56
Yang,
Z., Yang, Z., Yin, J., Qu, Z., 2018. A Risk-based Game Model for Rational Inspections
in Port State Control. Transportation Research Part E: Logistics and
Transportation Review, Volume 118, pp. 477–495
Yuan, C.C., Chiu, R.H., Cai, C., 2020. Important Factors Influencing the Implementation of Independent Port State Control Regimes. Journal of Marine Science and Engineering, Volume 8(9), pp. 1–15
Yuan, C.C., Chung, W.H., Cai, C., Sung, S.T., 2020. Application of Statistical Process Control on Port State Control. Journal of Marine Science and Engineering, Volume 8(10), p. 746