Special Issues
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Advanced Computational Methods and AI-Driven Optimization for Sustainable Industrial Systems: Towards Industry 5.0

Submission Deadline: 31 December 2026 View: 177 Submit to Special Issue

Guest Editors

Prof. Antonio Giallanza

Email: antonio.giallanza@unime.it

Affiliation: Department of Engineering, University of Messina, Messina, Italy

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Research Interests: human-robot collaboration, sustainable production systems, life cycle assessment (LCA), green design, industry 4.0 operations, predictive maintenance

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Prof. Giuseppe Marannano

Email: giuseppe.marannano@unipa.it

Affiliation: Department of Engineering, University of Palermo, Palermo, Italy

Homepage:

Research Interests: mechanical design, computational modeling, finite element analysis (FEA), multi-physics modeling, industrial optimization, metaheuristic algorithms

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Summary

The transition towards Industry 5.0 requires a paradigm shift centered on sustainability, resilience, and human-machine interaction, supported by the technological architectures of Industry 4.0. This evolution introduces complex engineering challenges that cannot be fully resolved using conventional approaches. This Special Issue aims to gather cutting-edge contributions that integrate advanced computational modeling, metaheuristic algorithms, and Artificial Intelligence (AI) driven methods to design, validate, and optimize complex mechanical systems and industrial processes. We are particularly interested in research demonstrating how numerical simulation and optimization algorithms can enhance the sustainability of production systems, the efficiency of Human Robot Collaboration (HRC), predictive maintenance, and complex logistics networks. The ultimate goal is to bridge the gap between theoretical computational modeling and real-world industrial applications.


Suggested themes include:
· Metaheuristic algorithms for industrial optimization
· Multiphysics modeling for green design
· Computational models for HRC
· Integration of Life Cycle Assessment (LCA) with numerical simulation
· Digital Twins for predictive maintenance
· AI application in industrial systems


Keywords

computational modeling, AI-driven optimization, industry 5.0, sustainable production, life cycle assessment (LCA), human robot collaboration, metaheuristic algorithms, predictive maintenance

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