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Sustainable Operations in Maritime Industry

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A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312).

Deadline for manuscript submissions: closed (10 March 2023) | Viewed by 21543

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Special Issue Editors

Dr. Yuquan (Bill) Du

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Guest Editor

Department of Maritime and Logistics Management, Australian Maritime College, University of Tasmania, Launceston, TAS 7250, Australia
Interests: scheduling models and algorithms; logistics and supply chain management; optimisation problems in ports and shipping

Dr. Gang Chen

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Guest Editor

Port and Shipping Management, World Maritime University, Malmo, Sweden
Interests: maritime logistics; port and ship operations using quantitative research methods

Prof. Dr. Shuaian Wang

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Guest Editor

Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: shipping network design; port operations planning; urban transport network modeling; sustainable transportation and green logistics
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Special Issue Information

Dear Colleagues,

We cordially invite you to submit a paper to our Special Issue entitled ‘sustainable operations in maritime industry’ of the Journal of Marine Science and Engineering, which is covered by SCIE and has an impact factor of 2.744 (JCR Q1 in the category of "Engineering, Marine").

With specific reference to maritime operations, the International Maritime Organization (IMO) noted with concern that the health of oceans and marine biodiversity are negatively affected by marine pollution from a number of land-based sources, including vessels and ports. To sustain the development of maritime transportation, “sustainable maritime,” which operates with a good balance between environmental impact and economic interests, has been the focus of marine industries and government agencies and are required to look into energy saving and reducing the impact of their operations on society and the environment.

This Special Issue on “Sustainable operations in maritime industry” to be published in the Journal of Marine Science and Engineering aims to bring together recent theoretical, applied or methodological advances concerning operations in the sustainable maritime industry, no matter they are hardware, software, or innovative approaches related, and encourage the researchers in maritime studies to have deep thoughts to make the maritime industry sustainable and green. Both research and review papers are welcomed.

Dr. Yuquan (Bill) Du
Dr. Gang Chen
Dr. Shuaian (Hans) Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sustainable maritime transportation operations
sustainable maritime network or fleet optimization
innovations for sustainability issues with emerging technologies
environmental issues in the maritime industry
sustainable maritime transportation and competitive advantages
sustainable maritime transportation technologies

Published Papers (11 papers)

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Editorial

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2 pages, 164 KiB

Open AccessEditorial

Sustainable Operations in Maritime Industry

by Yuquan Du, Gang Chen and Shuaian Wang

J. Mar. Sci. Eng. 2023, 11(5), 922; https://doi.org/10.3390/jmse11050922 - 26 Apr 2023

Viewed by 1434

Abstract

Maritime transportation serves as the backbone of international trade and the global economy [...] Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

2 pages, 169 KiB

Open AccessEditorial

Pathway of Mathematical Optimization Research: From Specialized Problems and Opaque Algorithms to Standardized Problems and Transparent Algorithms

by Shuaian Wang and Yuquan Du

J. Mar. Sci. Eng. 2022, 10(10), 1434; https://doi.org/10.3390/jmse10101434 - 5 Oct 2022

Cited by 1 | Viewed by 1006

Abstract

Mathematical optimization (MO) formulates a decision problem with a maximization or minimization objective and a set of constraints on the decision variables, and designs an algorithm to find the best solution [...] Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

Research

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15 pages, 308 KiB

Open AccessArticle

Green Technology Adoption and Fleet Deployment for New and Aged Ships Considering Maritime Decarbonization

by Yiwei Wu, Yadan Huang, Hans Wang, Lu Zhen and Wei Shao

J. Mar. Sci. Eng. 2023, 11(1), 36; https://doi.org/10.3390/jmse11010036 - 28 Dec 2022

Cited by 9 | Viewed by 2987

Abstract

Maritime decarbonization and strict international regulations have forced liner companies to find new solutions for reducing fuel consumption and greenhouse gas emissions in recent years. Green technology is regarded as one of the most promising alternatives to achieve environmental benefits despite its high initial investment costs. Therefore, a scientific method is required to assess the possibility of green technology adoption for liner companies. This study formulates a mixed-integer nonlinear programming model to determine whether to retrofit their ship fleets with green technology and how to deploy ships while taking maritime decarbonization into account. To convert the nonlinear model into a linear model that can be solved directly by off-the-shelf solvers, several linearization techniques are applied in this study. Sensitivity analyses involving the influences of the initial investment cost, fuel consumption reduction rate of green technology, unit fuel cost, and fixed operating cost of a ship on operation decisions are conducted. Green technology may become more competitive when modern technology development makes it efficient and economical. As fuel and fixed operating costs increase, more ships retrofitted with green technology will be deployed on all shipping routes. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

21 pages, 6626 KiB

Open AccessArticle

Speed Optimization of Container Ship Considering Route Segmentation and Weather Data Loading: Turning Point-Time Segmentation Method

by Xiaohe Li, Baozhi Sun, Jianhai Jin and Jun Ding

J. Mar. Sci. Eng. 2022, 10(12), 1835; https://doi.org/10.3390/jmse10121835 - 29 Nov 2022

Cited by 14 | Viewed by 2105

Abstract

As one of the ship energy efficiency optimization measures with the most energy saving and emission reduction potential, ship speed optimization has been recommended by the International Maritime Organization. In ship speed optimization, considering the influence of weather conditions, route segmentation and weather data loading methods significantly affect the reliability of speed optimization results. Therefore, taking the ocean-going container ship as the research object, on the basis of constructing the main engine fuel consumption prediction model and shaft speed prediction model based on machine learning methods, a route segmentation and weather loading-speed optimization iterative algorithm is proposed in this study. Single-objective speed optimization research is then conducted based on the algorithm. The research results show that the proposed algorithm effectively reduces the difference between optimized fuel consumption and actual fuel consumption, and can achieve a fuel-saving rate between 2.1% and 5.2%. This study achieves an accurate and reliable prediction of ship fuel consumption and shaft speed, and solves the strong coupling problem between route segmentation, weather loading, and speed optimization by iterative optimization of ship speed. The proposed algorithm provides strong technical support for ships to achieve the goal of energy saving and emission reduction. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

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25 pages, 7344 KiB

Open AccessArticle

Analysis of Ballast Water Discharged in Port—A Case Study of the Port of Ploče (Croatia)

by Nermin Hasanspahić, Marijana Pećarević, Niko Hrdalo and Leo Čampara

J. Mar. Sci. Eng. 2022, 10(11), 1700; https://doi.org/10.3390/jmse10111700 - 9 Nov 2022

Cited by 7 | Viewed by 3141

Abstract

Ballast water is recognized as a major vector for the transfer of Harmful Aquatic Organisms and Pathogens (HAOP) and a source of sea pollution that negatively affects the environment and human health. Therefore, the International Maritime Organization (IMO) adopted the International Convention for the Control and Management of Ship’s Ballast Water and Sediments (BWM Convention) in 2004. The BWM Convention introduced two standards, Ballast Water Exchange Standard (Regulation D-1) and Ballast Water Performance Standard (Regulation D-2). Ships are required to install Ballast Water Treatment (BWT) equipment in order to comply with Regulation D-2. However, the deadline for the installation of BWT is prolonged until September 2024, and many ships are still complying only with Regulation D-1. In addition, there are specific sea areas where Regulation D-1 cannot be complied with, and hence, HAOP could be easily transferred between ports. Consequently, it is essential to develop a system to protect the marine environment, human health and economy in coastal areas from the introduction of HAOP. This paper analyses ballast water discharged in the Port of Ploče (Croatia) according to ship type, age and flag they are flying. It was found that general cargo ships and bulk carriers discharged most of the ballast (87% of the total quantity) in the Port of Ploče. Moreover, discharged ballast water was analysed according to the origin, and it was found that 70% of discharged ballast originates from the Adriatic Sea. Based on the analysis of the research results and literature review, the ballast water risk assessment (BWRA) method was adopted, however, with certain modifications. The adopted method is modified by an additional risk factor (the deballasting ship’s age), different risk scoring of the deballasting ship type and adding Paris MoU Grey and Black lists flag ships as high-risk ships. As a result, the BWRA method presented in the paper could be used as an early warning system and to facilitate the implementation of adequate measures to prevent pollution by discharged ballast water. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

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16 pages, 312 KiB

Open AccessArticle

Pairwise-Comparison Based Semi-SPO Method for Ship Inspection Planning in Maritime Transportation

by Ying Yang, Ran Yan and Hans Wang

J. Mar. Sci. Eng. 2022, 10(11), 1696; https://doi.org/10.3390/jmse10111696 - 8 Nov 2022

Cited by 2 | Viewed by 1262

Abstract

Port state control (PSC) plays an important role in enhancing maritime safety and protecting the marine environment. Since the inspection resources are limited and the inspection process is costly and time-consuming, a critical issue for port states to guarantee inspection efficiency is to accurately select ships with a high risk for inspection. To address this issue, this study proposes three prediction models to predict the ship deficiency number and a ship selection optimization model based on the prediction results to target the riskiest ships for inspection. In addition to a linear regression model for ship deficiency number prediction solved by the least squares method, we establish two prediction models with the pairwise-comparison target based semi-“smart predict then optimize” (semi-SPO) method. Specifically, a linear programming model and a support vector machine (SVM) model are built and both have a loss function to minimize the sum of predicted ranking errors of each pair of ships regarding their deficiency numbers. We use the Hong Kong port as a case study, which shows that the SVM model based on the semi-SPO approach performs best among the three models with the least computation time and best ship selection decisions. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

16 pages, 1031 KiB

Open AccessArticle

Joint Planning of Fleet Deployment, Ship Refueling, and Speed Optimization for Dual-Fuel Ships Considering Methane Slip

by Yiwei Wu, Yadan Huang, H. Wang and Lu Zhen

J. Mar. Sci. Eng. 2022, 10(11), 1690; https://doi.org/10.3390/jmse10111690 - 8 Nov 2022

Cited by 10 | Viewed by 1669

Abstract

Reducing air pollution and greenhouse gas emissions has become one of the primary tasks for the shipping industry over the past few years. Among alternative marine fuels, liquefied natural gas (LNG) is regarded as one of the most popular alternative marine fuels because it is one of the cleanest fossil marine fuels. Therefore, a practical way to implement green shipping is to deploy dual-fuel ships that can burn conventional fuel oil and LNG on various ship routes. However, a severe problem faced by dual-fuel ships is methane slip from the engines of ships. Therefore, this study formulates a nonlinear mixed-integer programming model for an integrated optimization problem of fleet deployment, ship refueling, and speed optimization for dual-fuel ships, with the consideration of fuel consumption of both main and auxiliary engines, ship carbon emissions, availability of LNG at different ports of call, and methane slip from the main engines of ships. Several linearization techniques are applied to transform the nonlinear model into a linear model that can be directly solved by off-the-shelf solvers. A large number of computational experiments are carried out to assess the model performance. The proposed linearized model can be solved quickly by Gurobi, namely shorter than 0.12 s, which implies the possibility of applying the proposed model to practical problems to help decision-makers of shipping liners make operational decisions. In addition, sensitivity analyses with essential parameters, such as the price difference between the conventional fuel oil and LNG, carbon tax, and methane slip amount, are conducted to investigate the influences of these factors on operational decisions to seek managerial insights. For example, even under the existing strictest carbon tax policy, shipping liners do not need to deploy more ships and slow steaming to reduce the total weekly cost. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

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9 pages, 208 KiB

Open AccessArticle

On the K-Means Clustering Model for Performance Enhancement of Port State Control

by Zeyu Hou, Ran Yan and Shuaian Wang

J. Mar. Sci. Eng. 2022, 10(11), 1608; https://doi.org/10.3390/jmse10111608 - 1 Nov 2022

Cited by 3 | Viewed by 1735

Abstract

Nowadays, the concept of port state control is viewed as a safety net to safeguard maritime security, protect the marine environment, and ensure decent working and living circumstances for seafarers on board to a large extent. The ship can be detained for further checking if significant deficiencies are discovered during a port state control inspection. There is much research on this topic, but there have been few studies on the relationship between ship deficiencies and ship detention decisions using unsupervised machine learning artificial intelligence techniques. Although the previous methods or models are feasible for ship detention decisions, they all have shortcomings to some extent, such as large training model errors caused by the imbalance of class labels in the dataset and the fact that the training model cannot comprehensively consider all factors influencing ship detention decision due to the complexity and diversity of the problem. Unsupervised algorithms do not need to label all data in advance, and we can incorporate some fields related to port state control inspection data that can be collected into the model to allow the computer to automatically classify the ships at different risk levels according to relative criteria, e.g., the Tokyo memorandum of understanding, which may result in more objective results, thus eliminating the influence of subjective domain knowledge. It may also have more comprehensive coverage and more information on port state control inspection and decision models. Therefore, this research explores and develops an unsupervised algorithm based on k-means to improve port state control inspection decision-making models using the six-years inspection data from the Tokyo memorandum of understanding. The results show that the accuracy rate is around 50%. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

17 pages, 2288 KiB

Open AccessArticle

Efficiency Analysis of the Coastal Port Group in the Yangtze River Delta

by Siqin Yu, Lina Gong and Mingyun Qi

J. Mar. Sci. Eng. 2022, 10(11), 1575; https://doi.org/10.3390/jmse10111575 - 25 Oct 2022

Cited by 2 | Viewed by 1423

Abstract

In recent years, the coastal ports of the Yangtze River Delta have rapidly developed with the progress of science and technology, which has caused some problems on account of the rapid development of ports. On the one hand, there is fierce competition within the same port group; on the other hand, many ports waste resources. This study selected the three-stage data envelopment analysis (DEA) and Malmquist index models to calculate and analyze the efficiency value of the coastal port group in the Yangtze River Delta; the study was conducted to make a reference for the formulation of the optimization strategy from the perspectives of static and dynamic efficiency. The results show that from the perspective of static efficiency, the comprehensive efficiency of the Yangtze River Delta coastal port cluster is at the upper-middle level. However, it has not yet reached the frontier surface, and the low scale efficiency is why the port group has not been called the frontier surface. From the perspective of dynamic efficiency, the total factor productivity of the Yangtze River Delta port group has increased by 3.6% in the past five years. Technological progress and comprehensive technical efficiency have improved. The optimization strategy was formulated according to the problems faced by the Yangtze River Delta port group and the reasons for not reaching the frontier. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

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15 pages, 532 KiB

Open AccessArticle

Configuration Analysis of Factors Influencing Port Competitiveness of Hinterland Cities under TOE Framework: Evidence from China

by Zhenyu Huang, Ying Yang and Fengmei Zhang

J. Mar. Sci. Eng. 2022, 10(10), 1558; https://doi.org/10.3390/jmse10101558 - 20 Oct 2022

Cited by 3 | Viewed by 2124

Abstract

Attention is increasingly being paid to the influence of hinterland cities on port competitiveness, but in-depth research is lacking on the formation conditions and mechanism of hinterland cities’ influence on port competitiveness. Based on the technology–organization–environment (TOE) framework and the characteristics of Chinese government organizational behavior, in this study, we used fuzzy-set qualitative comparative analysis (fsQCA) to conduct a condition configuration analysis of 21 coastal ports and their hinterland cities in China. The findings showed the following: (1) The technology, organization, and environment conditions of hinterland cities cannot provide the necessary conditions for high or low port competitiveness alone: different combinations of these conditions have produced three high and four low port competitiveness configurations. (2) The three configurations of high port competitiveness are the organization–environment, economy–balance, and finance–balance types. Adequate government financial supply, high tertiary industry proportion, good economic development, and market openness are the core conditions required for achieving high port competitiveness. (3) The four configurations of low port competitiveness are finance–facilities–environment, capability–finance–environment, technology–finance–economy, and capability–industry–economy restrictions. Here, low-level innovation capability, inadequate government financial supply, and low tertiary industry proportion are the core conditions leading to low port competitiveness. We revealed the concurrent synergistic effect of the three conditions of technology, organization, and environment in hinterland cities and demonstrated the causal complexity and asymmetry of the impact of hinterland cities on port competitiveness. Our conclusions provide empirical evidence that will aid hinterland cities in formulating differentiated port competitiveness promotion policies according to their own conditions and endowments. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

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Other

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4 pages, 277 KiB

Open AccessPerspective

Integrating Shipping Domain Knowledge into Computer Vision Models for Maritime Transportation

by Ying Yang, Ran Yan and Shuaian Wang

J. Mar. Sci. Eng. 2022, 10(12), 1885; https://doi.org/10.3390/jmse10121885 - 4 Dec 2022

Cited by 2 | Viewed by 1517

Abstract

Maritime transportation plays a significant role in international trade and the global supply chain. To enhance maritime safety and reduce pollution to the marine environment, various regulations and conventions are proposed by international organizations. To ensure that shipping activities comply with the relevant regulations, more and more attention has been paid to maritime surveillance. Specifically, cameras have been widely equipped on the shore and drones to capture the videos of vessels. Then, computer vision (CV) methods are adopted to recognize the specific type of ships in the videos so as to identify illegal shipping activities. However, the complex marine environments may hinder the CV models from making accurate ship recognition. Therefore, this study proposes a novel approach of integrating the domain knowledge, such as the ship features and sailing speed, in CV for ship recognition of maritime transportation, which can better support maritime surveillance. We also give two specific examples to demonstrate the great potential of this method in future research on ship recognition. Full article

(This article belongs to the Special Issue Sustainable Operations in Maritime Industry)

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