# Robust Engineering for the Design of Resilient Manufacturing Systems
## 2 State-of-the-Art  
Simulation modelling and analysis is conducted in order to gain insight into these complex systems, testing new operating or resource policies and new concepts or systems before implementing them, as well as to gather information and knowledge without disturbing the actual system [9]. Simulation is a very helpful and valuable ICT tool in modern manufacturing. It provides decision-makers and engineers with a secure and low cost tool that provides fast analysis for the investigation of the system complexity as well as for identifying possible changes regarding the system configuration. Further to that with simulation, the operational policies which may affect the performance of the system or organization can also be examined minimizing intrusion, and thus cause disturbances to the physical/actual system [10]. Simulation models are categorized into static, dynamic, continuous, discrete, deterministic, and stochastic. Static models comprise a system of equations, which represent the state of the system under investigation in a specific point in time [7]. If the relationships that compose the model are simple enough, it may be possible to use mathematical methods, such as algebra, calculus, or probability theory, to obtain exact information on questions of interest; this is called an analytic solution. However, most real-world systems are too complex to allow realistic models to be evaluated analytically, and these models must be studied with the utilization of suitable simulation models [11]. The traditional role of simulation is to present a \"smart\" business with a significant competitive advantage during the development, deployment and implementation of its plans and strategies. Simulations are accomplished by virtualizing processes with the use of tools, testing virtual models prior to application in the real world and proving to help: (i) performance prediction, such as latency, utilization and bottlenecks, (ii) disclosure of how the various components of a system interact, (iii) experimenting with and evaluating the merits of alternative scenarios, (iv) providing a knowledge base of the system configuration, (v) serving as a valuable means of demonstration and as a consequence of the above and finally, to (vi) promote decision-making [12]. Discrete event simulation (DES) as a discrete sequence of events is a form of computer-based modeling of a system. Simulation has a number of advantages over other operational research (OR) techniques, including the ability to experiment with any aspect of a business system [13]. The term \"discrete\" refers to the simulation progressing through time at mutually exclusive intervals. A mechanism is required to track the evolution of time, taking into account the dynamic nature of the systems to be modelled. This is accomplished using a tool known as a \"simulation clock,\" which changes as events occur. As previously stated, DES models employ two prominent approaches as follows [11]: (i) Next Event Time Advances (NETA) and (ii) Fixed Increment Time Advances (FITA). Generally, NETA is more commonly used in simulation than FITA, because it is less complicated [12]. Finally, the effectiveness of DES and its flexibility result from its stochastic nature which makes it suitable for use in a wide range of applications, including warehouse operations, as a means of validating the performance of different indicators such as concerning material handling systems or order-picking and product location strategies [14]. Nonetheless, as computing processing power has increased, artificial intelligence (AI) has emerged, and IoT sensing has expanded in the modern warehouse, a new type of \"simulation\" paradigm known as the \"Digital Twin\" has emerged, allowing real-time control and digitization of a physical system [15; 16; 17]. Moreover, by modifying the production line for personalized production it is necessary to integrate Cyber Physical Production Systems to achieve the unique customer requirements and to achieve resilience [18].