## 2 Context and Research Objectives
### Amditions of CyberFactory#1 Project  
CyberFactory#1 project aims at designing, developing, integrating and demonstrating a set of key enabling capabilities to foster optimization and resilience of the FoF. It addresses the needs of pilots from transportation, automotive, electronics and machine manufacturing industries around use cases such as collaborative product design, autonomous machine reconfiguration, continuous product improvement, distributed manufacturing and real time situational awareness. It also proposes preventive and reactive capabilities to address cyber and physical threats and safety concerns to FoF [26].  
In comparison with other Industry 4.0 related projects, the differentiating factors of CyberFactory#1 approach are threefold. First, the system considered is neither a simple manufacturing asset nor a sum of isolated assets, but a network of factories, which is considered in a System of Systems (SoS) approach [27]. The second challenge is to propose novel architectures, technologies and methodologies to optimize the level of efficiency and security of this SoS in a context where every step towards digitization exposes the manufacturing process to widening cyber-threats [28]. Third, the goal is to solve more than the technological challenges of Industry 4.0 by properly addressing the following dimensions of digital transformation:  
* the technical dimension, which includes cyber-physical system modeling [29], real-time sensing and tracking capabilities [30] and human/machine access and trust management [31];* the economic dimension, which includes modeling of the factory ecosystem [32], manufacturing data-lake exploitation [33] and the adversarial/robust machine learning algorithms [34];
* the human dimension, which includes human behavior modeling [35], to optimizing the human/machine interactions [36] and implement mutual behavior watch between machines and humans; and
* the societal dimension, arising from the previous three, includes the factory modeling as a System of Systems, the distributed manufacturing's design and, finally, the cyber-resilience mechanisms' definition.  
Across these four dimensions, three layers have been defined to address, respectively:  
* the simulation challenge (main object of this article);
* the optimization challenge (one main area of application); and
* the resilience challenge (second area of application.  
The simulation and modeling layer acts both as a challenge of its own and an enabler for optimization and resilience challenges. Indeed, it is proposed to use a comprehensive FoF simulation capacity to support FoF system architecture validation, as well as development, testing and validation of optimization and resilience capabilities.  
Figure 1 illustrates the relationships among the physical plant, the modeling and simulation capacity, the optimization capacity and the resilience capacity, where obviously the FoF DT and the manufacturing the data lake act as shared resources to support optimization and resilience capacities.  
In this paper, we address specific limitations of state-of-the-art modeling and simulation techniques and propose innovations which may unleash the potential for exploitation of DTs in support of rational decision making for FoF optimization and resilience.