## 1 Introduction  
Real-world production systems are characterized by the presence of multiple stakeholders, each pursuing their own objectives, particularly the fulfillment of specific performance metrics [1, 2, 3, 4]. Strategic management in such distributed systems aims to achieve global goals but often neglects the interests of individual agents. Consequently, managerial decisions will, first, face resistance from individual agents within the system and, second, lead to a deterioration in these agents' performance metrics. For example, profit maximization may be achieved by reducing the quality of materials or increasing production volumes, which ultimately leads to an increase in the proportion of defects and a corresponding decrease in demand for products, which sooner or later will lead to a drop in profits. However, in the short term, such solutions may dominate over more pragmatic options involving the allocation of resources in terms of work in all areas of the production chain. The above necessitates the development of tools for identifying sustainable solutions that enable both the system and its agents to achieve their target objectives [1; 2; 3; 4]. However, the design of decision support systems for complex production systems introduces a mathematical challenge rooted in the inherent complexity of solving such problems. Certain tasks in this domain are classified as NP-complete problems, i.e., problems with nondeterministic polynomial-time complexity [5]. These problems may lack stable solutions or require significant time and computational resources to resolve. This requires the use of complex algorithms to obtain satisfactory solutions [6; 7]. To address these challenges, the use of a multi-agent approach is proposed. A distinctive feature of the multi-agent approach lies in transitioning from passive agents, modeled as object classes in object-oriented paradigms, to active intelligent agents capable of perceiving their environment and responding to its changes. Each agent in such a system possesses unique characteristics, goals, and thus, behavioral strategies. Software-implemented models based on the multi-agent approach represent specialized AI tools that can be integrated into decision support systems across various domains of economic activity [8; 9]. The application of the multi-agent approach in the context of the problem under study is driven by the necessity to account for the inherent multi-agent nature of production systems, at least in terms of reconciling the diverse objective functions of individual agents and the system as a whole. Therefore, the analysis of multi-agent interactions, the development of adequate models for such interactions, and the design of practice-oriented decision support methods and informational frameworks for managing multi-agent systems require thorough investigation, as their outcomes may directly enhance economic activities, particularly through the rational allocation of resources to achieve the required economic indicators or, in the form of an inverse problem, to calculate the need for resources and their redistribution. [4; 9; 10; 11; 12]. Existing multi-agent systems that represent organizational systems do not offer a universal model of organizational production structure. They provide ontologies to address the resource allocation problem, considering entities of the production process, but neglect the specifics of organizational structure [8; 10]. Other solutions, such as ADEPT [9], allow for the management of business processes but propose systems with a limited set of role types. The list of roles they offer enables a comprehensive description of stakeholders in a real organization only at the level of a single enterprise, without covering other levels of system consideration (such as enterprise networks or employees of a single enterprise), and does not take into account the diversity of external stakeholders not part of the organization.