This study focuses on the development of a decision support system for complex production systems. As a promising approach to resource allocation challenges, the application of AI tools, particularly the multi-agent approach, is proposed. It is hypothesized that a decision support system based on multi-agent systems (MASs), grounded in an invariant ontology and utilizing game-theoretic tools, will enhance the effectiveness of managerial decisions by accounting for the inherent multi-agent nature of complex production systems, manifested in diverse objective functions and intricate interaction structures. The work identifies key features of intelligent agent interactions in multi-agent systems. Drawing on interaction types relevant to complex production systems and the selected MAS implementation architecture, an ontological model of multi-agent interactions and an ontological model for managing complex production systems are designed. The proposed models are tested using data from an oil and gas enterprise, and a formalization of utility functions for its agents is provided using game-theoretic tools. The resulting ontological model of multi-agent interactions in complex production systems is practical and easy to implement. It enables the design of MASs for diverse complex production systems, as demonstrated through a resource management case study in the oil and gas industry. Future study will refine the ontological model via industry-specific validation and expand the mathematical and computational tools for model implementation.

Current decision support systems for recommending labor resource allocation and generating production schedules in the systems with decentralized technological process control often fail to account for the impact of participants’ communication strategies on the shifts in target performance indicators, which depend on the alignment between local goals of production units and the global objectives of the system. The goal of the present study is to develop an approach for determining optimal communication parameters among intelligent agents to achieve system-level performance targets using the previously developed multiagent systems (MAS) for optimizing technological processes. The research investigates how agent constraint systems influence both overall system welfare and the individual welfare of agents, considering the shifts in their objective functions driven by preferred communication strategies. A workflow is developed to identify effective constraints. Using this workflow, the study provides recommendations for assigning regional field development plans, accounting for participants’ tendencies toward cooperation. On data where the potential for increasing the region’s flow rate through optimization of labor resource allocation and scheduling of well intervention operations (GTO) does not exceed 6%, the presented solution enabled the development of field plans that result in an additional 1% increase in the predicted oil production region’s flow rate on top of the gain achieved through resource allocation optimization.