Project start: 1.10.2025.

Companies must apply efficient planning and optimization methods for production processes to maintain competitiveness in the market and adapt more quickly to market needs.In recent years, there has been a visible trend in the industry towards increasing the level of production modularity, which results in more complex and numerous material flow routes between modules.This opens up not only the need for greater flexibility but also an increase in the number of transportation systems. In such an environment, solving the problem of synchronizing the operation of different transportation systems is one of the key moments for achieving production efficiency.

As the complexity of production planning and management problems grows,along with difficulties in finding solutions using mathematical models within a reasonable timeframe,there is an increasing interest in applying simulations. Today,discrete simulation tools enable the creation of very complex simulations and analysis of complex production systems. However, simulations alone do not provide optimal solutions, but with their help,it is possible to assess the system's behaviour under given conditions.On the other hand,metaheuristic and hybrid algorithms are capable of searching large and complex problem spaces and finding high-quality solutions with a high probability. Often, the optimization problem is very difficult or even impossible to represent through a mathematical model, which limits the use of optimization algorithms alone. Combining simulations with advanced optimization algorithms can significantly improve the quality of solutions and use computational resources more efficiently.Combination of simulations and optimization is rarely applied,this is mostly due to the complexity of the problems and the lack of practically applicable tools. As part of this research, an attempt will be made to offer a framework for addressing the challenges of integrating simulation and independently developed optimization tools.