AI-Driven Optimization Under Symmetry and Uncertainty in Smart Manufacturing Systems

Dear Colleagues,

Smart manufacturing systems, ranging from additive manufacturing (3D printing) to real-time production scheduling, operate under complex conditions characterized by structural symmetry, evident in modular machines and interchangeable resources, alongside dynamic uncertainty manifesting in demand fluctuations, sensor noise, and machine failures. At the intersection of AI and operations research, this Special Issue explores how symmetry-aware and uncertainty resilient optimization methods can enhance the efficiency, reliability, and autonomy of Industry 4.0 systems.

We invite original research and review papers that introduce novel AI methodologies such as graph neural networks, deep reinforcement learning, fuzzy‐random theory, and symmetry exploiting metaheuristics for optimizing critical manufacturing tasks including process planning, scheduling, predictive maintenance, and control systems. This issue’s scope encompasses studies that address symmetric design principles, uncertainty quantification techniques, and multi-objective trade-offs balancing throughput against energy consumption and robustness requirements. We particularly encourage work that demonstrates integration with digital twins, edge computing architectures, or cyber-physical systems to showcase real-world applicability.

Submissions may span the full spectrum from theoretical foundations and algorithm development to comprehensive simulation studies and industrial case studies that validate proposed approaches. The overarching goal of this issue is to advance our understanding of how symmetry and uncertainty can be jointly leveraged by AI methodologies to push the boundary of intelligent manufacturing, creating systems that are both structurally efficient and operationally resilient in the face of complex industrial challenges.

This issue aims to establish a unified framework where symmetry-exploiting AI methodologies inherently incorporate uncertainty resilience, advancing autonomous and adaptive manufacturing systems that maintain optimal performance under both structural regularities and operational variabilities.

In this Special Issue, the topics covered in this section include (but are not limited to) the following:

• Data-driven approaches for industrial process optimization;
• Intelligent decision-making under uncertainty in industrial processes;
• Hybrid AI and operations research methods for production planning;
• Symmetry-aware production and job scheduling;
• Robust/stochastic optimization under uncertainty;
• AI in symmetric resource allocation and control;
• Graph-based models for modular manufacturing;
• Reinforcement learning under partial observability;
• Fuzzy and stochastic modeling in process planning;
• Digital twins and edge AI for real-time adaptive systems;
• AI-driven predictive maintenance and quality assurance;
• Adaptive control and learning in manufacturing operations.

Dr. Achraf Touil
Dr. Rachid Oucheikh
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. Symmetry 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 2400 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.

• symmetry
• uncertainty
• optimization
• smart manufacturing
• Industry 4.0
• artificial intelligence
• robust optimization
• graph neural networks
• reinforcement learning
• additive manufacturing
• digital twins