Dear Colleagues,

Within the vision of Industry 4.0, in the nearer future, complex problems due to planning, scheduling and control of production and logistic processes are derived by data-driven decisions that will enable manufacturing companies to accurately predict and plan their activities on the machine, the plant, as well as at the supply chain-level. In recent times, data-driven decision support based on Simulation, integrated Operations Research models, Advanced Data Analytics, Computational Intelligence, and AI are changing how modern manufacturing processes are planned and executed. The potential of such novel technologies is also being expanded by the interaction with smart sensors, the Internet of Things (IoT), cloud computing, and Cyber-Physical Systems (CPS), which made it possible to realize the “digital twin” (DT) of a product, system, and process. Despite the term, DT might be old and known to simulation experts, the growing searches and attention from worldwide companies (including consulting companies mentioning DT in the technological roadmaps for the current digital transformation) toward the DT concept arises new questions about how the theoretical soundness of the DT concept and how it can be implemented in practice. Despite many people may think that DTs are simulation models, a simulation model may not necessarily be a DT. Digital models used in simulations often have the same type of sensor information and controls as a DT, but the information may be generated and manipulated within the simulation in an offline fashion. The simulation may replicate what could happen in the real world, but not necessarily what is currently happening. If the digital model is fed with an automated one-way data flow between the physical and digital objects, e.g., a simulation model using real-time sensor data as inputs, this has been referred to as the digital shadow. In production and logistics, hybrid simulation (defined as models that combined at least two traditional simulation approaches, e.g., DES + ABS) is very popular to model complex enterprise-wide systems. Recently, other concepts, such as “Big Simulation”, were proposed as an evolution of currently distributed simulations to take big data input and produce big data output in near to real-time. The so-called Symbiotic Simulation has recently emerged as an interesting framework for integrating simulation with Digital Twin, IoT and Big Data. However, the concept is still in its infancy and requires considerations, for example, in terms of direct data collection from the sensors in a cyber-physical system or in terms of running constantly in the background to monitor and attempt to improve the performance of the system via simulation. The human factor is also poorly discussed when it comes to a representation in the digital twin of a manufacturing or production system. This is the ultimate maturity stage of Digital Twins, but still, the body of knowledge is limited and theoretical advances and practical solutions showing and discussing insights from real case studies are needed.

We invite authors to submit scientific papers that approach the aspects of integrating simulation, continuous/discrete optimization, human factors and decision support models based on simulation and distributed intelligence into the digital twin of manufacturing and logistics systems. Submissions involving case studies and innovative applications in the field of smart manufacturing and logistics systems are welcomed. Both empirical and conceptual, quantitative and qualitative original research studies are welcomed. Case studies and practical applications are encouraged. To that end, we seek submissions with an original perspective and advanced thinking on the development of the smart manufacturing and logistics field, instead of theoretical studies and frameworks on simulation-digital twin integration. Although they can contain some review of the literature, we look for submissions that go beyond systematic reviews and propose and discuss fresh conceptual and methodological avenues for further development of the field.

The topics of interest include, but are not limited to:

• Definition of the role of simulation for digital twins in manufacturing and logistics
• Digital twin maturity model for clear identification of simulation capabilities at each stage
• Human factors and human digital twins
• Real-time (or near real-time) execution of simulation models for digital twin implementation
• Digital twin synchronization with the real manufacturing/logistics system counterpart
• Data analytics integration
• Digital twin for decision support in production and logistics
• Distributed and Edge Intelligence
• Mathematical optimization, heuristics and metaheuristics for simulation-based digital twins
• Verification and validation of simulation models in digital twins
• Integration of Simulation with Virtual, Mixed and Augmented Reality for Digital Twin Applications
• Digital twin of stochastic production environments

Dr. Vittorio Solina
Dr. Antonio Padovano
Dr. Francesco Longo
Dr. Giovanni Mirabelli
Guest Editors

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