Intelligent Decision Support Systems and Data Analytics in Maritime Supply Chains
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 28 April 2027 | Viewed by 82
Editor
Interests: maritime supply chains; mathematical modelling; optimization; predictive analytics; port logistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The maritime industry is essential for international trade, but it currently faces increasing operational volatility, environmental requirements, congestion, uncertainty, resource allocation problems and complex logistical constraints. In this context, mathematical modelling, operations research, statistical learning and intelligent decision support systems provide new opportunities to analyze, predict and optimize decision-making problems in maritime supply chains.
This Special Issue focuses on recent advances in mathematical, statistical, numerical and computational methods for supporting decision-making in maritime supply chains, with particular emphasis on modelling, optimization, simulation, predictive analytics and intelligent decision support systems. Contributions may address theoretical, numerical, computational, or applied developments in ports, shipping, terminal operations and multimodal logistics networks.
Topics of interest include, but are not limited to:
- Mathematical models for maritime supply chain decision-making;
- Optimization and simulation methods for port, shipping, terminal and multimodal logistics operations under uncertainty;
- Stochastic programming, dynamic programming, multi-objective optimization, game-theoretic models and queuing theory;
- Applied statistics, machine learning, time-series forecasting and predictive analytics for maritime operational performance;
- Graph-theoretic, Bayesian, fuzzy logic and spatio-temporal models for risk, resilience, disruption, cybersecurity and supply chain vulnerability;
- Mathematical and data-driven approaches for sustainability, emissions reduction, energy efficiency, alternative fuels, predictive maintenance and green maritime logistics;
- Real-world case studies with clear methodological contributions in maritime logistics.
Dr. Claudia Durán San Martín
Guest Editor
Manuscript Submission Information
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Keywords
- mathematical modelling
- maritime supply chains
- optimization and simulation
- stochastic programming
- machine learning
- predictive analytics
- port operations
- risk and resilience
- green maritime logistics
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