## 1 Introduction
Reviewing the literature of both methodological innovations and algorithmic solutions allows us to conclude that the production scheduling methodologies proposed so far have not focused on the influence of the personal predispositions of employees and the individual characteristics of the tools on the time and cost of a technological operation. Admittedly, there are methodologies that allocate individual units to operations [35]. However, these methodologies do not emphasize the direct functional relationship between the selected combination of workers and tools and the time and cost of the operation. Apart from being relatively simple and local in the sense of time solutions that implement the learning and training effect, there is no more advanced work that would take into account the impact of the total characteristics of the resource combination assigned to the production operation on its duration. As a result, it is not possible to freely take into account such factors as the employee's skills, their experience with working with a specific tool, interaction in the employee team, and interaction between machines and tools. Of course, research has been conducted on the existence of the influence of the selection of production resources on the course of operations [19; 36; 37]. However, the measurable effect of such a solution that takes into account this phenomenon in scheduling is still an interesting research topic. The proposed general methodology for production scheduling, including the influence of the employee and the tools, focuses on this effect and turned out to be more effective in terms of minimizing the maximum completion time and the cost of the production process compared to existing solutions. The efficiency of the proposed new scheduling methodology was assessed using examples of different types of technological processes to check the influence of the type of production. To determine the actual effect of the solution, taking into account the influence of production resources, the research was carried out on the basis of a dedicated adaptation of the Monte Carlo optimization algorithm. The total resistance of the applied brute-force algorithm to getting stuck in a local minimum ensured the obtained experimental results are independent of an optimization algorithm's imperfections. However, it should be emphasized that the proposed solution is a new general methodology. The algorithm itself is not an integral part of the proposed solution, and the universal methodology developed will ensure significant benefits when combined with most of the optimization algorithms that cope well with complex hypersurface solutions and the presence of numerous local minima. The paper consists of a theoretical introduction, a description of methodologies, research results, and a summary. Section 2 of the article describes the new proposed production scheduling methodology, the core of which are modifications to the feasibility constraint, as well as the maximum completion time and cost criteria. This section also describes the laboratory algorithm dedicated to compare the standard and the new methodology. Section 3 presents the applied research methodology based on the use of a virtual production plant. Section 4 discusses the procedure for carrying out the experimental studies, the results obtained for the four production processes, and the discussion of these results. The last section contains a summary and conclusions.