# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 4 Methodology  
The concept of Industry 4.0 uses a cyber-physical system (CPS). This system is a computer system in which a mechanism is controlled or monitored by computer-based algorithms. Assume CPS is a system where its individual parts are managed according to optimised plans. Each essential part of the system is virtually represented in a DT and its operations are managed according to the synchronisation with the other parts of the modelled system (the concrete settings of this simulated part of the production system are represented by the solution candidate). Most simulated objects in the system (parts of the DT) often do not have sufficient computing power to perform their own simulation optimisation. They are interconnected and have the technology to transfer and download data. Therefore, there is no need to perform optimisation using them. The simulation optimisation is performed by remote computers (with sufficient capacity). The settings of the modelled system parameters are sent to the connected physical objects to manage their behaviour. Simulation optimisation using a DT can be very time consuming. Another problem is the large number of data generated during the simulation experimentation with a DT. The proposed methodology reduces the volume of data generated in the simulation optimisation by combining different approaches used in the global optimisation, programming, and big data techniques. The methodology tries to avoid the problem of generating big data rather than trying to extract information from the huge amount of generated data. We performed many optimisation experiments in the initial stage of testing, and we confirmed that the selection of the appropriate optimisation method could significantly affect the number of simulation experiments. A common situation in industrial simulation optimisation is that we cannot test all the possible solution candidates in the search space in most cases as it is an NP-hard problem. The next problem is to validate if the optimisation method finds the global minimum or maximum of the objective function of the simulation model (DT). The main advantage of using the optimisation method (especially metaheuristic methods) is a significant reduction of the number of tested (generated) solution candidates in the simulation optimisation process. The proposed methodology is described using the AGVs DT representing the transport from the warehouse to the production lines by AGVs. The simulation optimisers running on different servers simulate different variants of the modelled logistic system and use different optimisation techniques to find the best way of conveying the parts with high utilisation of the tractors with trailers. We developed a system using client-server architecture where the client application controls all the running simulation optimisers on the connected remote computers in parallel. These server simulation optimisers launch the simulation on the simulation software. The server simulation optimiser (or optimisers launched on the computer) can perform the optimisation experiments with a discrete-event simulation model sent by the client application. Each simulation optimiser can use one of the implemented optimisation methods with the setting specified by the client application. The simulation optimiser, therefore, imitates the decision-making process of an object in an individual part of the simulated system with a defined optimisation strategy, which tries to find its own best possible solution to the simulated problem. For example, the AGV decides how to efficiently convey all the parts to all the locations on the production lines in the shortest amount of time. The next flowchart shows the several basic phases of the proposed methodology of parallel simulation optimisation--see Figure 7. The principle of running the optimisation experiment using the remote simulation optimiser is described in the flowchart--see Figure 9 in Section 5 Simulation Optimiser. Figure 7: Flowchart of methodology of the parallel simulation optimisation.