# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 1 Introduction  
The increasing use of digital twins (DTs) is one of the most important trends in the Industry 4.0 concept and industrial engineering [1], and some authors directly refer to the Industry 4.0 era as the era of DT [2]. The concept of Industry 4.0 extends the possibilities and use of DTs for, e.g., decision support and production planning [3], solving unexpected situations/problems or predicting such situations [4], as well as training and knowledge transfer of leadership, management, and executives [5, 6].  
In addition to DTs, the use of cyber-physical systems (CPSs) is increasingly mentioned in connection with the Industry 4.0 concept. A CPS is a system consisting of physical entities controlled by computer algorithms that allow these entities to function completely independently, including autonomous decision making, i.e., they can control a given technological unit or be an independent member of complex production units. CPSs are often built on artificial intelligence and machine learning [6], using simulations [7] for decision making and other areas of computer science that are being developed within the Industry 4.0 concept.  
The goal of our research is to test and modify different optimisation methods (and also their settings) suitable for discrete simulation optimisation in accordance with the Industry 4.0 concept. A large number of data are generated during the simulation experimentation with a DT. This paper describes a proposed methodology for reducing the volume of generated data in a simulation optimisation performed with a DT. The methodology attempts to avoid the problem of generating big data rather than trying to extract information from the large volume of generated data (often representing inappropriate solutions). We were inspired by different techniques used in the field of big data problems. We had to solve many problems during the simulation experimentation, e.g., reduction of the generated data while maintaining the amount of necessary information without the loss of accuracy needed for the simulation optimisation; ensuring a high search speed of these data; using the parallelisation of the DT simulation experimentation; reducing the redundant data; preventing possible data loss during the simulation experimentation with DT, etc.  
We used various optimisation methods (stochastic, metaheuristic-population or neighbourhood-based algorithms), which are used in the optimisation of processes of the DT. The proposed methodology was validated on several simulation studies (both theoretical and practical). We selected the case of a DT of automated guided vehicles (AGVs) conveying different types of parts to assembly lines from warehouses to describe the methodology proposed in this paper.  
The effectiveness of different optimisation methods in finding a suitable solution is not the same. It differs especially in the case of different objective function landscapes (the objective function of the DT is the goal we want to achieve). The paper describes the evaluation of simulation experiments using different optimisation methods. These evaluations are analysed from different perspectives (quality of found solution, the speed of finding the solution, etc.). The effectiveness of the optimisation method also depends on both the principle of the method and the settings of its parameters. We tested different settings to compare the effectiveness of the optimisation methods. If the optimisation method is set up well, it does not need to perform many simulation experiments, and thus, the number of generated data is reduced.