Manuscript Topics

The rapid advancement of modern technologies is transforming manufacturing systems—and other complex systems—into highly interconnected, intelligent, and adaptive environments. Among these technologies, digital twins have emerged as a powerful paradigm for representing and synchronizing physical systems with their virtual counterparts in real time. By integrating data, models, and advanced analytics, digital twins enhance system visibility, enable predictive capabilities, and support more informed and optimization-driven decision-making.

At the same time, optimization and simulation have become essential methodologies for analyzing and improving complex manufacturing systems characterized by uncertainty, stochastic behavior, and dynamic interactions. The integration of digital twin technologies with optimization and simulation methods offers significant opportunities to improve operational efficiency, system resilience, and sustainability in smart manufacturing environments.

We invite original research articles that present novel approaches, models, algorithms, and applications related to digital twin-enabled manufacturing systems.

We invite original research articles that present novel approaches, models, algorithms, and applications related to digital twin-enabled manufacturing systems, with particular emphasis on optimization modeling, stochastic systems, and decision-making methodologies in digital twin-enabled environments. Potential topics include, but are not limited to:

• Digital twin architectures and frameworks for smart manufacturing
• Simulation modeling and analysis of modern manufacturing systems
• Simulation optimization methods for production planning and scheduling
• Integration of digital twins with optimization and decision support systems
• Multi-state manufacturing system modeling and optimization
• Network optimization in manufacturing and logistics
• Real-time monitoring and control using digital twins
• Resilient and adaptive manufacturing systems
• Reliability and resilience optimization
• Digital twins for predictive maintenance and reliability analysis
• Applications of digital twins in production logistics and supply chains
• Sustainable and energy-efficient smart manufacturing systems
• Stochastic optimization in digital twin systems

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