# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 2 Literature Background
### Optimisation Methods  
Simulation optimisation techniques are mostly used for the following:  
* Discrete event simulation;
* Systems of stochastic nonlinear and/or differential equations [20].  
There are many algorithms for simulation optimisation, and their use depends on the specific application. In their paper, the authors [PERSON] et al. [21] describe the following as the basic algorithms for simulation optimisation:  
* Ranking and selection;
* Black-box search methods that directly work with the simulation estimates of objective values;
* Meta-model-based methods;
* Gradient-based methods;
* Sample path optimisation;
* Stochastic constraints and multi-objective simulation optimisation.  
Considering the applicability of simulation optimisation in many industries and aspects of human activities and its generality, there are many studies using simulation optimisation in areas of industrial product development such as maintenance systems [22], industrial production lines [23], new product development [24; 25], etc.  
Among the random optimum search methods, techniques such as simulated annealing [26], genetic algorithms [27], stochastic ruler methods [28], ant colony optimisation [29], tabu search [30], etc. are used for the optimisation. For most of these algorithms, there is evidence of global convergence, i.e., convergence to a global solution or local convergence [31].  
The paper [32] analyses a significant sample size (663 research papers) to provide insights about the past and present practices in discrete simulation-based optimisation (DSBO) applied to industrial engineering. DSBO is a set of tools and methods commonly used to help researchers and practitioners, for analysis and decision making, for investment and resource allocation in new or already existing systems. Many articles published in this area refer to the solution of a specific problem using one or more methods. The following table presents the state-of-the-art techniques applied to DSBO projects on IE problems (NP-hard), showing the past and present practices and bringing up possibilities for the future of DSBO on IE--see Table 1.  
Many authors focus on the optimisation of a specific problem with one optimisation method or a modified derived optimisation method. Unfortunately, they do not address the applicability of multiple optimisation methods to multiple problems and the comparison of the effectiveness of these methods using the different evaluation criteria (not just using the average, mean, or standard deviation). We tested different optimisation methods (stochastic, metaheuristic-population, or neighbourhood-based algorithms) solving different problems in industrial engineering (logistics, production planning, etc.) using discrete-event simulation models. We were also interested in the evaluation of optimisation experiments with different settings of the optimisation methods (big volume of data) from different perspectives [33]. We proposed a methodology that can be applied to the optimisation of several industrial engineering problems represented by a discrete-event simulation model.  
The proposed methodology for parallel simulation optimisation uses the server's simulation optimiser (architecture client-server) using stochastic, neighbourhood-, and population-based optimisation methods. Using the right method can significantly reduce the search space as it reduces the data generated in the optimisation process.