Healthcare systems worldwide are facing unprecedented challenges driven by demographic changes, ageing populations, chronic diseases, healthcare workforce shortages, pandemics, climate-related crises, and increasing expectations regarding quality, accessibility, sustainability and resilience. These challenges require innovative approaches capable of supporting healthcare organizations in designing, planning, managing and continuously improving healthcare services.

The digital transformation of healthcare has opened new perspectives by combining Operations Research (OR), Artificial Intelligence (AI), Industrial Engineering (IE), data analytics, optimization, simulation, digital twins and decision support systems. These technologies enable healthcare organizations to improve operational performance while ensuring patient-centered care and efficient resource utilization.

This track aims to gather researchers and practitioners working on innovative methods, models and applications addressing healthcare management problems at operational, tactical and strategic levels. Particular attention will be devoted to the integration of optimization methods, AI techniques and industrial engineering approaches for supporting healthcare decision-making in hospitals, home healthcare, emergency medical services and emergency departments, territorial healthcare, logistics and crisis management.

The track welcomes theoretical contributions, methodological developments, real-world applications, interdisciplinary research and industrial case studies.

The special/invited sessions/tracks SIMCA are annually organized since 2015 within IFAC INCOMs, MIMs, and World Congresses. The SIMCA Open Invited Track aims to bring together scientists working in all branches of control theory to discuss, in the light of manufacturing control problems, issues relating to development of the theory and methodology of identification, corresponding mathematical problems, parameter and non-parametric identification, structure identification and expert analysis, problems of selection and data analysis, control systems with an identifier, identification in intelligent systems, simulation procedures and software for identification and modeling, cognitive issues of identification, verification and problems of software quality for complex systems, global network resources of support processes of identification, modeling, and control.

Currently, there is no generally accepted definition of the concept of "identification of the manufacturing system". It is intuitively clear that this concept is extremely broad in scope, and the term "manufacturing system identification" can denote a large number of processes that are different in their properties and quantitative characteristics. Therefore, scientific studies of the problem of identification, which do not study certain types of identification and do not take into account the engineering context of their implementation, allow us to determine only the general characteristics of identification. With such a general approach, many problems of real or potentially possible identifications of manufacturing systems that are significant for engineering practice are outside the scope of scientific research.

The dynamics of the growth of scientific knowledge about the identification of manufacturing systems suggests that the possibilities of a general approach as a methodological basis for the creation of new identification algorithms are now almost completely exhausted. This hypothesis is also supported by the fact of the practical complete absence of detailed numerical studies of the characteristics and information capabilities of identification algorithms.

This state of scientific knowledge about identification is especially unacceptable at the present time, when there is every reason to believe that the Russian industry needs systemic modernization. The technical renewal of obsolete production assets is always associated with the commissioning of new, more efficient main technological equipment (control object) and, accordingly, with the commissioning of a new, more efficient management system. In this case, it is often necessary to put the control system into operation (or, at least, complete its installation) simultaneously with the commissioning of the main technological equipment, which in many cases is impossible without organizing the identification of the control object.

Difficulties in posing and solving the problem of identification are mainly due to the fact that the subject of identification is the team of ACS (automatic control system) developers, and more importantly, the identity is a system object. Due to the systemic nature of identification, a systemic paradox arises in the identification process, a description of which was given back in the late 1960s. XX century in the work of A.V. Balakrishnan and V. Peterka. However, a scientifically based method of getting out of this paradox has not yet been developed, which undoubtedly indicates the difficulty of solving the problem of identification.

To increase the applied significance of scientific research on identification, an intellectual approach to the identification problem is proposed, based on four principles:

• on system-functional modeling of the intellectual activity of a team of developers in identification processes
• on the recognition of the decisive role of the human factor in the processes of identification;
• on consideration of identification as a system object and as a necessary component of a certain type of engineering practice for creating ACS;
• on the definition of identification algorithms based on the formulation and solution of maximin problems of statistical synthesis of optimal identification algorithms.

The main goal of this work is to develop an intellectual approach to the problem of identification of manufacturing systems. Apparently, various conceptual models of identification are possible and, in particular, models of the conditions for the emergence of identification within the framework of the engineering practice of creating ACS.

Firstly, it is considered that identification is implemented at the pre-project stages of creating an ACS before the development and approval of the terms of reference for creating an ACS.

Secondly, it is believed that the decision to start identification is made only if the development team has:

• there is no reliable a priori information about an adequate mathematical model of manufacturing systems for the purpose of ACS design;
• there is only a set of working hypotheses about the belonging of the indicated adequate model to the given families of mathematical models, parameterized by vector parameters with a given set of allowable values in the Euclidean or functional space.

Thirdly, it is considered that the given families of mathematical models are chosen in such a way that empirical estimates of vector parameters can be obtained using traditional methods of parametric and nonparametric identification.

A manufacturing system, in order to study the laws of functioning of which the team of developers is forced to organize identification, we will call a poorly studied control object. A poorly studied control object is considered to be a real object, in relation to which the development team does not have reliable a priori information about an adequate mathematical model for the purpose of ACS design, and there is only a set of working hypotheses about the belonging of an adequate mathematical model to given families of mathematical models.

Automation of a poorly studied control object always includes solving the problem of choosing the "best" hypothesis from a given set of working hypotheses about an adequate mathematical model of the control object. It seems that the stages of choosing the "best" working hypothesis and creating a new set of working hypotheses will continue until the degree of knowledge of the laws of the functioning of the manufacturing system reaches a level at which this object, from the point of view of the developers, ceases to be a poorly studied control object.

Based on the above ideas, the identification of a manufacturing system (briefly, identification) is an iterative process, each iteration of which includes the following main stages.

1. Finding a method for generating a set of working hypotheses about an adequate mathematical model of the manufacturing system for the purpose of ACS design.
2. Formation of a set of working hypotheses about an adequate mathematical model of the manufacturing system for the purpose of ACS design.
3. Finding a synthesis method for an algorithm for choosing the "best" hypothesis from a given set of working hypotheses.
4. Synthesis of an algorithm for choosing the "best" working hypothesis.
5. Determination of the "best" working hypothesis based on the developed selection algorithm and a given set of experimental data.
6. Finding a method for analyzing the "best" working hypothesis in terms of the requirements of the terms of reference for the creation of ACS.
7. Analysis of the "best" working hypothesis in terms of the requirements of the terms of reference for the creation of ACS.

The refinement of this definition is connected with the expansion of its content due to a detailed description of the final and intermediate goals of identification, its composition and structure within the framework of the conceptual model of a certain type of engineering practice of creating ACS. It seems that such a refinement can be obtained on the basis of the application of systematic and intellectual approaches. The idea of using a systematic approach as a methodological basis for setting and solving identification problems is not new to the scientific literature in the field of identification. According to his ideas, the problem of experimental construction of a mathematical model of the control object for the purpose of designing the ACS (identification problem) and the problem of synthesizing the algorithm for the functioning of the controller based on the given mathematical model of the control object (optimization problem) cannot be solved autonomously, in isolation. Their formulations and solutions are causally related to each other, since they are interrelated system tasks in the process of creating an ACS that meets the requirements of the terms of reference.

Based on these ideas, the following definitions can be given. The ultimate goal of identification for the ACS design goal is to find an adequate mathematical model of the manufacturing system, i.e. a mathematical model, on the basis of which it is possible to carry out such a synthesis of the algorithm for the functioning of the regulator, that, based on the results of this synthesis, it is possible to design an ACS that meets the requirements of the technical task. The ultimate goal of identification is to find an adequate family of mathematical models of manufacturing systems, parameterized by a vector parameter with a given set of valid values in the Euclidean or functional space. An adequate family of mathematical models is a set of mathematical models, on the basis of which, in the process of parametric or nonparametric identification, it is possible to determine an adequate mathematical model of a manufacturing system.

Thus, manufacturing control problems, which the track is devoted to, relates to development of the theory and methodology of identification, corresponding mathematical problems, parameter and non-parametric identification, structure identification and expert analysis, problems of selection and data analysis, control systems with an identifier, identification in intelligent systems, simulation procedures and software for identification and modeling, cognitive issues of identification, verification and problems of software quality for complex systems, global network resources of support processes of identification, modeling, and control.