This study addresses the foundational step of developing a classification and taxonomy of agent objective functions as a prerequisite for analyzing stability and forming robust production schedules in distributed manufacturing systems. The research is based on the premise that instability or insufficient robustness in scheduling solutions often arises from the neglect of the inherent multi-agent nature of real-world distributed production systems. These systems are characterized by the presence of multiple decision-making entities, each pursuing its own objectives or performance indicators. Since strategic management in such systems is typically oriented toward achieving global system-level goals, it often overlooks the interests of individual agents. As a result, the implemented decisions may encounter resistance from specific agents and lead to deterioration in the performance of their individual objective functions. These features underline the need to develop tools for identifying robust solutions, in which both the system as a whole and its constituent agents can achieve sustainably high performance across their respective objectives. The aim of this study is to analyze the divergent objective functions of management agents in distributed manufacturing systems in the context of forming robust production schedules. The research explores typical objective functions of structural units within the production system and presents their classification in terms of constraints, nature, granularity, behavioral orientation, and inter-agent dependency. The outcomes of the study include a comprehensive taxonomy of agent objective functions, along with the selection of relevant game-theoretic models for each pair of agents based on their interaction strategies. The findings contribute to the development of methodological and technological tools for decision support in sustainable manufacturing, extending current research on intelligent agent modeling and coordination in complex production environments.
Burlutskaya et al. (Thu,) studied this question.