Topic Editors

1. Former Professor, International Port and Logistics Department, Tongmyung University, Busan 48520, Republic of Korea
2. Logi AI Innovation Lab, T2L, Koyang 1545, Republic of Korea
Department of Management, College of Business, Maurer Center 312, Bowling Green State University, Bowling Green, OH 43403, USA

Global Maritime Logistics in the Era of Industry 4.0

Abstract submission deadline
30 November 2024
Manuscript submission deadline
28 February 2025
Viewed by
1030

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

  • AI
  • machine learning
  • optimization
  • environmental issues
  • maritime logistics
  • Industry 4.0

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai
- - 2020 20.8 Days CHF 1600 Submit
Digital
digital
- - 2021 22.7 Days CHF 1000 Submit
Journal of Marine Science and Engineering
jmse
2.9 3.7 2013 15.4 Days CHF 2600 Submit
Logistics
logistics
3.8 5.1 2017 25.4 Days CHF 1400 Submit
Systems
systems
1.9 3.3 2013 16.8 Days CHF 2400 Submit

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Published Papers (1 paper)

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15 pages, 2990 KiB  
Article
Unrelated Parallel Machine Scheduling Problem Considering Job Splitting, Inventories, Shortage, and Resource: A Meta-Heuristic Approach
Systems 2024, 12(2), 37; https://doi.org/10.3390/systems12020037 - 24 Jan 2024
Viewed by 618
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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