## 1 Introduction  
Intelligent Logistics Systems (ILSs) play a crucial role in the revolution of Industry 4.0 by leveraging advanced technologies to enhance supply chain management and logistics operations. Logistics operations are cardinal in manufacturing systems. Today, designing production systems is practically impossible without using computer simulation. Simulation techniques have proven themselves to be one of the most practical methodologies available to investigate and evaluate manufacturing issues [1]. Simulation techniques provide a virtual environment to model, analyze, and optimize production systems before implementing changes in the real world. Additionally, production planning and control play a key role in enabling manufacturers to gain visibility and control over all manufacturing activities. Simulation models are usually used when complex or complicated systems and processes must be analyzed [2]. In relation to this, ref. [3] conducted an empirical survey on large-scale modeling and simulation of physical systems to investigate research needs and current challenges. Through advanced modeling, scenario testing, and visualization tools, simulation programs provide a comprehensive framework for optimizing production processes. Optimization can be carried out manually, based on experiments and testing scenarios, or, on the other hand, by using various optimization tools. The authors of [4] studied the integration of simulation and optimization for the design and improvement of facility layouts. They reviewed a few alternative layout designs, taking into account production and logistics constraints. Based on the Recursive Optimization-Simulation Approach, ref. [5] developed a methodology to provide effective decision support for integrated production planning and scheduling. Optimizing human-robot task allocation using a simulation tool was presented in [6]. Mathematical and simulation-based optimizations were applied in the research in [7]. While the mathematical model was formulated to find an optimal production-distribution plan, the simulation model verified the feasibility of the plan. Similarly, in [8], a mathematical model was proposed to optimize the production processes, with a simulation model to test the alternative facility designs. The study in [9] used a discrete event simulation model to test investment alternatives regarding their impact on selected production indicators. The authors of [10] performed experiments to optimize the production program using the FlexSim simulation program and the OptQuest to obtain a cost-effective solution in a short time. Applications of Tecnomatix Plant Simulation 2402 software to optimize the production system can be found in [11; 12; 13]. It is important to mention that the integration of simulation models with virtual reality holds significant importance in the implementation of Industry 4.0. The article in [14] deals with using the Tecnomatix Plant Simulation 2402 software to visualize, analyze, and optimize the modeled production-assembly process in connection with virtual reality. On the other hand, some researchers focused on the development of optimization tools. For instance, the authors of [15] developed the open-source software JSOptimizer 1.0 that can be used to optimize simulation models of complex engineering systems built with JaamSim. While effective, manual optimization can be time-consuming and may not always yield the global optimum. It is also necessary to emphasize that this approach is highly dependent on the executor's expertise and the specific problem at hand. The second method of optimization is using optimization tools that are commonly integrated into simulation programs. Using optimization tools such as genetic algorithms (GAs) and Experiment Manager (EM) introduces a more automated and exploratory approach. In industry, optimization mainly concerns the production process. For instance, a study by [16] utilized the Experiment Manager to set up scenarios and experiments for optimizing manufacturing processes.