Global Maritime Logistics in the Era of Industry 4.0

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closed (30 November 2024)

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closed (28 February 2025)

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Topic Information

Dear Colleagues,

The maritime industry is experiencing a profound transformation powered by cutting-edge technologies. Artificial Intelligence (AI) and Machine Learning (ML) have become the bedrock of shipping, port, and logistics operations. Their profound impact is seen in the streamlining of processes, predictive demand forecasting, and the optimization of cargo handling, all while fortifying security measures. AI and ML play a pivotal role in cost reduction and efficiency enhancement, from predicting equipment maintenance requirements to dynamically planning ship routes.

Environmental stewardship is at the forefront of the maritime agenda. The urgent need to curtail emissions to meet global climate goals has prompted the adoption of clean energy sources and shore power facilities at ports. Ships are making substantial investments in cleaner propulsion technologies, embracing alternative fuels and fuel-efficient vessel designs to reduce the industry’s carbon footprint.

The automation wave extends to the open sea as well. Autonomous vessels are revolutionizing global transport. Equipped with state-of-the-art sensors, AI-driven navigation systems, and remote monitoring capabilities, these vessels promise heightened safety, decreased labor costs, and exceptional efficiency, often making critical decisions without human intervention.

As cargo volumes surge, ports are integrating automation into their operations. Automated cranes, self-driving vehicles, and intelligent container management systems are becoming the norm, significantly expediting cargo handling while reducing delays and human errors. These advancements culminate in an exponential increase in efficiency across the maritime sector.

Innovation is the heartbeat of this industry. Visionaries are perpetually seeking novel strategies to augment productivity and efficiency. Experimental approaches, such as blockchain for supply chain transparency, 3D printing of critical spare parts while at sea, and drone-assisted cargo inspections, are pushing the boundaries of what is possible. The emergence of “smart ports” underscores the potential of integrating technology to create seamless, efficient logistics hubs.

The maritime logistics sector is inextricably intertwined with AI, automation, sustainability, and ingenious solutions in this dynamic landscape. As it continues to evolve, it faces future challenges and opportunities with unbridled optimism and enthusiasm. The future of maritime logistics is bright and full of promise.

As the port and maritime logistics sector evolves, integrating AI, automation, sustainability, and creative solutions promises a bright future. It is an exciting journey of navigating challenges and seizing opportunities.

Prof. Dr. Nam Kyu Park
Prof. Dr. Hokey Min
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600
Digital digital	-	3.1	2021	28.4 Days	CHF 1000
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.4 Days	CHF 2600
Logistics logistics	3.6	6.6	2017	28.5 Days	CHF 1400
Systems systems	2.3	2.8	2013	19.6 Days	CHF 2400

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Published Papers (4 papers)

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All AI Digital JMSE Logistics Systems

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32 pages, 3326 KiB  

Open AccessArticle

Topological Resilience of Shipping Alliances in Maritime Transportation Networks

by Tiago Novaes Mathias, Yoshihisa Sugimura, Tomoya Kawasaki and Yasuhiro Akakura

Logistics 2025, 9(1), 6; https://doi.org/10.3390/logistics9010006 - 3 Jan 2025

Viewed by 894

Abstract

Background: The resilience of shipping alliances within the global maritime container network (GMCN) has critical implications for global trade. This study examines the topological robustness of strategic alliances (SAs) in liner shipping, focusing on their structural properties and responses to disruptions. Methods [...] Read more.

Background: The resilience of shipping alliances within the global maritime container network (GMCN) has critical implications for global trade. This study examines the topological robustness of strategic alliances (SAs) in liner shipping, focusing on their structural properties and responses to disruptions. Methods: Using pre-2019 container vessel schedule data, we constructed a weighted node-edge network model of the GMCN. Centrality metrics were computed to identify critical nodes, and simulations were conducted to evaluate network robustness under random failures (RFs) and targeted attacks (TAs). Results: The results highlight the GMCN’s scale-free topology, which fosters resilience against RFs but exposes vulnerabilities to TAs on high-centrality nodes like Singapore and Shanghai. Among alliances, 2M and Ocean demonstrated superior resilience, attributed to strategic port selection and extensive coverage, yet their reliance on central hubs presents significant risks. Conclusions: This study underscores the importance of alliance-specific strategies, such as infrastructure investments and redundancy planning, to mitigate vulnerabilities. By bridging gaps in existing literature, the research provides insights for policymakers and industry stakeholders to enhance the robustness and adaptability of maritime logistics networks. These findings contribute to ensuring stable global supply chains within an evolving trade environment. Full article

(This article belongs to the Topic Global Maritime Logistics in the Era of Industry 4.0)

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32 pages, 13228 KiB  

Open AccessArticle

Multi-Scale Higher-Order Dependencies (MSHOD): Higher-Order Interactions Mining and Key Nodes Identification for Global Liner Shipping Network

by Yude Fu, Xiang Li, Jichao Li, Mengjun Yu, Xiongyi Lu, Qizi Huangpeng and Xiaojun Duan

J. Mar. Sci. Eng. 2024, 12(8), 1305; https://doi.org/10.3390/jmse12081305 - 1 Aug 2024

Cited by 2 | Viewed by 1063

Abstract

Liner shipping accounts for over $80\%$ of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system [...] Read more.

Liner shipping accounts for over $80\%$ of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system of international trade, thereby supporting liner shipping as an effective framework for analyzing international trade and geopolitical trends. Traditional methods based on first-order global liner shipping networks, operating at a single scale, lack sufficient descriptive power for multi-variable sequential interactions and data representation accuracy among nodes. This paper proposes an effective methodology termed “Multi-Scale Higher-Order Dependencies (MSHOD)” that adeptly reveals the complexity of higher-order interactions among multi-scale nodes within the global liner shipping network. The key step of this method is to construct high-order dependency networks through multi-scale attributes. Based on the critical role of high-order interactions, a method for key node identification has been proposed. Experiments demonstrate that, compared to other methods, MSHOD can more effectively identify multi-scale nodes with regional dependencies. These nodes and their generated higher-order interactions could have transformative impacts on the network’s flow and stability. Therefore, by integrating multi-scale analysis methods to mine high-order interactions and identify key nodes with regional dependencies, this approach provides robust insights for assessing policy implementation effects, preventing unforeseen incidents, and revealing regional dual-circulation economic models, thereby contributing to strategies for global, stable development. Full article

(This article belongs to the Topic Global Maritime Logistics in the Era of Industry 4.0)

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39 pages, 1380 KiB  

Open AccessArticle

The Principal-Agent Theoretical Ramifications on Digital Transformation of Ports in Emerging Economies

by Benjamin Mosses Sakita, Berit Irene Helgheim and Svein Bråthen

Logistics 2024, 8(2), 51; https://doi.org/10.3390/logistics8020051 - 8 May 2024

Cited by 3 | Viewed by 2068

Abstract

Background: Scholarly literature indicates a slow pace at which maritime ports fully embrace digital transformation (DT). The reasons to this are largely anecdotal and lack solid empirical grounding. This inhibits an overall understanding of DT’s tenets and the development of evidence-based policies [...] Read more.

Background: Scholarly literature indicates a slow pace at which maritime ports fully embrace digital transformation (DT). The reasons to this are largely anecdotal and lack solid empirical grounding. This inhibits an overall understanding of DT’s tenets and the development of evidence-based policies and targeted actions. Methods: This study deployed a qualitative case study strategy to unpack the challenges of undertaking DT through the lens of principal-agent theory (PAT). Results: Analysis of data collected through 13 semi-structured interviews from a port’s value chain stakeholders revealed five thematic challenges that contradict successful implementation of DT. These included interagency constraints and system ownership tussles; system sabotage and prevalent corruption; prevalent human agency in port operations; cultural constraints; and political influence on port governance. Conclusions: To address these challenges, the study proposes a four-stage empirically grounded DT strategy framework that guides both practitioners and policymakers through DT endeavors. The framework includes: (1) the port’s value chain mapping, (2) stakeholder engagement, (3) resource mobilization, and (4) effective monitoring. For scholars, we provide an avenue for testing statistical significance of association and causality among the identified challenges. Full article

(This article belongs to the Topic Global Maritime Logistics in the Era of Industry 4.0)

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15 pages, 2990 KiB  

Open AccessArticle

Unrelated Parallel Machine Scheduling Problem Considering Job Splitting, Inventories, Shortage, and Resource: A Meta-Heuristic Approach

by Mohammad Arani, Mohsen Momenitabar and Tazrin Jahan Priyanka

Systems 2024, 12(2), 37; https://doi.org/10.3390/systems12020037 - 24 Jan 2024

Cited by 3 | Viewed by 2464

Abstract

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). [...] Read more.

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities. Full article

(This article belongs to the Topic Global Maritime Logistics in the Era of Industry 4.0)

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