This paper aims to develop a coordination-control model for hierarchical technogenic systems that strengthens goal alignment, enhances stability of control processes, and ensures resilient decision-making under uncertainty and cyber-related risks. The proposed approach integrates procedures of goal alignment, hierarchical coordination of local strategies, and cognitive assessment of operators into a unified decision-support framework. The methodology includes formal modelling of the state and target spaces, construction of an objective function for coordination, and expert-based evaluation of operator characteristics using a modified Delphi method. The model is validated through a simulated crisis scenario reflecting real industrial conditions. The model enables structured generation of coordination strategies in the terminal control cycle and supports identification of crisis-related risks across hierarchical levels. Experimental validation indicates reduced probability of operator errors and improved decision-making speed. The proposed model extends traditional coordination and DSS approaches by ensuring compatibility with modern digital infrastructures. Its architecture supports integration into smart factories, cyber-physical systems, energy and transport networks, and AI-driven management platforms. The results contribute to digital transformation by improving resilience, enhancing real-time coordination, and reducing the impact of human-related uncertainties in hierarchical control processes.
In a technogenic hierarchy (including production, transport, organizational and administrative management units, printing, and media), the formation of the security system structure is based on systemology and goal-oriented decision-making methods aimed at solving crisis-related problems. The analysis of emergency and risk situations under the influence of active threats and attacks has demonstrated the importance of developing models of coordination strategies for decision-making in hierarchical technogenic structures. Such models enhance the resilience of these systems to external negative impacts, threats, and attacks, and improve management eficiency through the implementation of inter-level integration and stratification of the control process.
At the current stage of the development of technological systems, it is characteristic that control decisions are made at diferent levels of the hierarchy, ranging from automatic control systems (ACS TP) to operational control by personnel and coordination management by higher levels. At the same time, higher levels do not always possess the appropriate level of professional and specialized training and often lack understanding of the content of technological situations during changes in the modes of supply of energy and material resources, as well as the influence of both external and internal disturbing factors. Particularly dangerous is the factor of misunderstanding that, when technological processes are brought to boundary modes with outdated equipment having reduced operational resources, emergency situations may arise. The solution to this situation is the development of a decision support system (DSS), the structure of which includes system experts, systems of intelligent data processing, and information-measuring systems for automatic database population.
The proposed approach forms a new type of coordination model for hierarchical technogenic systems, which significantly expands modern research by integrating goal coordination, cognitive characteristics of the operator, and strategy generation algorithms into a single formalized structure. Unlike existing models of coordination management, traditional DSS, and cognitive schemes, the proposed model simultaneously considers: the state of the control object, the target space of all levels of the hierarchy, the level of information and intellectual readiness of the operator, as well as risk factors that arise in crises. The novelty of the approach lies in the introduction of formalized intervals of cognitive operations and procedures for goal compatibility, which allows building strategies that are adaptive to uncertainty, structural disturbances, and threats to cyber-physical infrastructure. The practical advantages of the model are manifested in increasing the accuracy of the choice of control actions, reducing decision-making time, reducing the risk of incorrect or incompatible strategies, as well as significantly reducing the influence of the human factor due to clearly structured criteria for operator assessment and automated formation of strategies in crises.
The growing scientific interest in the issues of coordination control in hierarchical technogenic systems
is driven by the increasing complexity of modern cyber-physical and socio-technical systems. Studies
by Ukrainian scholars [
Modern approaches to modeling coordination control combine methods of systems analysis,
decision theory, game theory, and intelligent modeling, as demonstrated in [
Study [
An important role in contemporary research is played by the cognitive approach to modeling
management processes, where decision-making is viewed as an interaction among subjects with diferent
levels of competence. In the monograph [
In [18], the authors present models of operational control for technological production processes within complex hierarchical systems and economic networks under conditions of emergencies and natural disasters. Meanwhile, research [19] focuses on a decision support system designed for risk identification and assessment in complex hierarchical management environments. Drawing on the theoretical foundations and practical insights proposed in these works, it becomes possible to enable eficient real-time planning and synchronization of management processes across all system components — encompassing both structured management teams and unorganized human groups operating in high-risk or crisis situations.
Considerable attention is also given to optimization-based approaches in modeling coordination strategies. Studies [20, 21] propose the use of evolutionary algorithms, stochastic optimization, and fuzzy logic for adaptive alignment of goals across diferent management levels. Such methods allow the formation of consistent solutions even under incomplete information or dynamic system parameters.
Within the framework of systems analysis of complex technogenic objects, works [22, 23] discuss the concept of multilevel management, which integrates strategic, tactical, and operational decision-making levels. The authors note that improving the resilience of hierarchical systems requires developing feedback procedures that ensure adaptation of strategies to the current state of the controlled object.
Modern manufacturing is increasingly characterized by complex processes and the need for highprecision real-time data for efective decision-making. Studies [ 24, 25] investigate Manufacturing Execution Systems (MES) as a critical link between enterprise-level planning and shop-floor operations, providing functionalities such as production scheduling, batch tracking, and quality management. The Industrial Internet of Things (IIoT) addresses challenges in obtaining accurate data on the state space of the controlled object and its goal-oriented state space. This is achieved through automated data generation based on sensors and intelligent coordination management using machine learning and neural network models. Such systems can predict the outcomes of managerial decisions and adjust strategies in real time.
Article [26] presents a comprehensive literature review of recent developments in anomaly detection methods for identifying security threats in cyber-physical systems. In industrial control networks (ICNs), issues related to safety and reliability are of paramount importance, particularly due to the increased connectivity to the Internet, which raises the vulnerability of critical infrastructure. This has made incidents targeting pipelines and power grids more frequent and severe, especially in the context of the growing number of cyberattacks and terrorist threats.
In summary, despite a significant number of studies devoted to coordination in hierarchical systems, issues of formalizing coordination decision-making strategies in the context of technogenic systems with high levels of uncertainty remain insuficiently explored. Further development of a mathematical framework is needed to align the state space of the control object with the system’s goal space, ensuring lfexible strategic management across all hierarchy levels.
The coordination of subsystems of the n-th level of the hierarchy defines such a control action on the subsystems that forces them to function coherently in accordance with their local goals in such a way that the entire system achieves the overall objective. Since the lower-level systems have their own goals, which may not coincide with the goals of the upper hierarchical levels, conflicts may arise over resources, control strategies, and goal orientation, leading to the impossibility of achieving the global goal.
The actions of the strategic coordinator are aimed at • decomposition of the global goal into local ones; coordination of strategies for achieving goals and terms of implementation; • coordination of resource distribution for all hierarchical levels; • distribution of decision-making authority for each level of the hierarchy and the determination of priorities; • formation of a set of ranked quality criteria of control associated with risk optimization, the level of resource consumption, and guarantees of goal achievement.
The concept of coordination is associated with the procedures of goal-oriented decision-making and the evaluation of success in achieving the goal based on the decomposition of the control problem: ∃StratDcom(), ∃Strat () ; ∀ ∈ ⊂ , (1) where ∃Π : ∀ (, ) , (, ) = is the solution of the -th problem with respect to the goal at the terminal time, at which () ∈ . Then ∃ ⊂ { } ∃ (||), which order the sequence of problems {︀ 0 , 1 , . . . , }︀ , under (|), − min , ⊂ , where: Π – rule or algorithm for solving the problem; – current time; Strat () – problemsolving strategy; – terminal time; StratDcom () – decomposition strategy of the problem; – allowable time; – coordinating signal from the set of control actions; – control action time; {}, – solvable problem; – control implementation time; – target region; (||) – coordination strategy of control actions to achieve goal .
Accordingly, it is possible to distinguish classes of signals according to their functional purpose: (|=1) – classes of signals from each level that determine the state of objects and strategies of the lower level; ( |) – classes of control signals directed from the upper level to the lower one, which are formed based on the results of solving current control problems .
The eficiency of control in a hierarchical system is based on inter-level integration and stratification.
Definition 1. Integration — hierarchical ordering during the unification of systems with the purpose of organizing operational functioning and increasing eficiency in achieving the goal. Accordingly, the coordination of interacting subsystems improves the way of achieving the objective at all hierarchical levels, in accordance with the goal achievement strategy based on the choice of the procedure for searching the scheme of solving the control problem.
The problem of finding solutions in the target space conjugated with the state space is based on the search for mappings ( × ) → ( × ), for which we have.
{︃ : → , = + 1, ∃ , ∃^ ∈ ;
∀ ∈ : (^) ≤ () , where : → , : → . where – the set of all solutions for the states of the system (control object) under control and time ; – the set of admissible solutions, ⊂ , ∈/ ; – the objective function ( = ⨂︀ ); – the emergency (failure) region; – the cost of achieving the goal at time ; – the output function as a model of the control process; – the control quality functional; Ω – the set of uncertainties in the state of the control object; – the tolerance function, for which the following relations hold: (2) (3) (4) ∀ (, ) ∈ [ × Ω] ∃ .
(, ) ≤ (Ω) , : Ω → ,
thus, we obtain the condition of a satisfactory solution to the control problem [
Let us construct the state space and the target space accordingly (Figure 1a, b), on which we define the cone of control trajectories.
1 0 WCi Ci t
Ci b) trak t
The objective function can be defined considering the set of influencing factors in the form of mappings on the target space: { : × Ω → , : × Ω× → } ↦→ ⟨ (, ) = (, , (, ))⟩ , (5) where – is the objective function on Ω – the domain of uncertainty (both structural and parametric), which depends on the control strategy, the problem situation, and the decision-making procedures.
Definition 2.
[ ⊂ ⊂ ] ifned, for which the existence condition of the solution holds: ︀{ , ∈ }︀ =1 from the set of solutions and the mapping { : → , ∀ ∈ , ∀ ∈ }, are de∃ ∈ : : () = , ∈ () in the goal space of the system () ⊂ ×
for the terminal time – based on the scheme of selecting strategies ( + ), hat ensure its achievement within the permissible time is called a Decision Support System (DSS) if a family of problems (Figure 2).
Problem I. Synthesis of the Coordination System. If a global problem is given, along with the procedure for its decomposition into diferent levels, it is necessary to find such a problem for which a common coordinating strategy exists, based on which control signals are generated for all levels: ∃ ( | ∈ ) , ∃Π : ( → |=1) , Π → Strat (|) .
Problem II. Selection of the Method, Procedure, or Coordination Algorithm. If the structure of the system is given and conjugated with respect to the target problem, it is necessary to find an efective method or algorithm for obtaining (forming) a coordinating signal that would ensure the coherent behavior of the system to achieve the goal:
( ( ) ↔ Strukt ( )) ↦→ [∃Strat ( || ) : ∈ ] . coordinating strategy exists:
Problem III. Modification of Strategies. If the hierarchical system is not coordinated with respect to the problem ( × ), it is necessary to find such a modification of the problem for which a {∃Strat ( || ) : ∈/} ⇒ Π : ︁( Strat1→−Strat . ︁)
Problem IV. Decomposition of the Global Problem. If only the global problem is formulated, it is necessary to establish procedures for dividing it into classes of upper- and lower-level problems, so that the strategy for their solution is coordinated with respect to the upper-level problem. (6) (7) (8) Situation
Problem task
Factors x ZDi
PSrtorabtleRmZDX solver
ZDXi
Strategy Generator (Strat)
Z
Implementation of the problem
solution R(ZDXi TD )
WC trakz(t)
no z ÎWC
yes
If the problem is formulated at the upper hierarchical level, the challenge arises of finding a scheme for its solution, which involves two aspects: 1. Searching for or generating a strategy for solving the coordination control problem; 2. Synthesizing a new system structure according to the goals and coordination strategy, or modernizing and organizing the existing system according to the problem-solving scheme and procedure.
In accordance with these conditions, the scheme for selecting coordination strategies is constructed (Figure 3): • based on the problem situation in the i-th cycle, the problem is formulated; • according to the coordination principle, target control problems are generated within the hierarchy; • the compatibility condition of goals is checked, and the problem-solving strategies are selected from the knowledge base, and the coordination control scheme is built according to the hierarchical levels and type of structural organization.
The proposed model of coordination management is organically integrated into the modern IT environment, focused on digital transformation and intelligent decision-making support technologies. The architecture of the model involves the use of digital sensors and information and measuring systems for automated formation of the state space, machine learning modules for predicting crisis scenarios, as well as intelligent risk analysis algorithms for coordinating management strategies between hierarchy levels. The results of real-time data processing allow the DSS to ofer optimal management actions and reduce dependence on subjective operator decisions. Such integration ensures the compliance of the model with modern trends in AI-driven management, increases the level of automation, and makes it compatible with digital platforms for managing technogenic and cyber-physical systems.
As a result of the conducted research, based on the implementation of a game-based coordination strategy model for a hierarchical control system, the following factors influencing the emergence of crisis-related risks were identified: • factors related to assessing the state of controlled objects at the terminal stage of the technological process because of detected errors;
Problem formulation 2 Generation of task
solution goals 1 no yes yes
( ) yes
END Problem situation i-problem cycle Knowledge
base Coordination
principle Postulate of goal
compatibility across hierarchical
levels
Selection of problem-solving
strategy Selection of new structure
Structure development Construction of coordination
control scheme
Scheme implementation • factors associated with deviations from operational modes within the technological production process and with non-compliance with key parameters defined by regulatory documentation; • factors resulting from inconsistencies between the structure and functions of the control system and the cognitive as well as physiological-psychological characteristics of the decision-maker; • factors arising from errors in operation manuals for technogenic equipment or untimely updates of these manuals in accordance with changes in the operating modes of ACS TP, ACS, and IASU units; • factors related to insuficient professional knowledge of the operator-manager and the lack of systemic thinking.
The study involved systemic and logic-cognitive procedures used by operational personnel to assess problematic situations that must be addressed to mitigate the risk of accidents within cognitively intensive managerial activity. The research results are presented in Table 1.
The cognitive characteristics of the decision-making operator (DMO) in crisis situations were determined based on expert assessments derived from testing conducted at technogenic energy enterprises in the Lviv and Ivano-Frankivsk regions of Ukraine. These characteristics reflect the operator’s ability to resolve complex crisis situations under conditions of threats and attacks. For the analysis of the
Class of Operations Code Identification of risk factors Formulation of a problem situation based on data assessment Development of a decomposition process for the problem situation and execution of tasks Identification of problem-related tasks Generation of goal-oriented hypotheses ℎ Selection of task-solving strategies Management of the problem-solving pro- cess Analysis of results and coordination of goal- achievement processes Selection of arithmetic and logical opera- tions for implementing problem tasks Formation of a structural organization of operations for implementing an action program aimed at solving the crisis-related task Identification of crisis situations during the task-solving process Selection of methods for identifying the structure and dynamics of the object Selection of methods and tools for data ac- quisition and their assessment Ability to process heterogeneous data Intellectual interpretation of situational patterns within the system DMO’s cognitive reactions, the following types of crisis situations were considered: • resource failures (material or energy-related); • operational mode deviations of objects, loss of reliability of units, or errors made during task correction; • personnel disorientation caused by incorrect operator instructions.
In accordance with the above requirements, knowledge and skill tables were developed to characterize the DMO’s performance in crisis situations (Table 2). Based on the tests assessing the level of informationtechnology competence in hierarchical technogenic systems, the components and their permissible values required for the operator—as a cognitive agent—to implement the system’s crisis management process were determined.
Table 2 presents the knowledge-based framework of requirements necessary to ensure crisismanagement capabilities under the influence of threats, attacks, and internal conflicts within operational and administrative control systems. Based on the research results obtained in this study, it becomes possible to conduct the preparation and selection of individuals who will make decisions in the management of hierarchical technogenic systems.
To verify the reliability of the results, an expert assessment was conducted with the participation of 18 specialists (operators, ASC engineers, and technical safety specialists). The cognitive characteristics of the operators were assessed using the modified Delphi method using a 10-point scale normalized to the interval [0; 1]. The values of the intervals in Tables 1–2 were calculated by the method of normalization and averaging of expert assessments, with further stability verification through confidence intervals. The model was validated based on a simulated crisis scenario (sudden deviation of the technological process parameters and operator error). A comparison of the two modes – without DSS and with the use of a coordination model – showed a reduction in the risk of emergency decisions by 31% and a reduction in response time by 17–22%, which confirms the efectiveness of the proposed approach.
According to the definition of interlevel integration of the control system for hierarchical technogenic structures presented in this study, an important stage in constructing a decision-making model is the coordination of interacting systems to solve strategic tasks–achieving goals at all levels of the hierarchy. To accomplish this, it is necessary to implement new models in the hierarchical control system that are based on procedures for searching schemes for solving control problems under critical situations. For the training of personnel – decision makers (DMs) – it is essential to develop game-based simulation models of critical situations within the control system to develop their cognitive skills for responding under crisis conditions and to prevent the occurrence of emergency situations at the terminal control cycle. Under real operating conditions of a hierarchical technogenic structure, the model of coordination of decision-making strategies must determine the state space of the control object at the terminal cycle and align it with the target space at all levels of the control hierarchy.
The decision support system for crisis situations is built based on situation analysis and selection of control strategies aimed at goal orientation and minimization of the risk of emergency situations. For the correct selection of control strategies in the model of coordination strategies for decision-making, it is necessary to have a mathematical basis for the state spaces of control objects and the target spaces for strategic management of the technogenic system, which is formed based on the professional data and knowledge base. Therefore, an important stage in the implementation of such models is the creation of professional game-based models and the training of decision makers.
Compared to existing approaches – models based on game theory, multi-agent systems, and fuzzyoriented DSS – the proposed coordination model demonstrates the advantage of combining target compatibility, operator cognitive characteristics, and hierarchical coordination of strategies, which is rarely implemented in a comprehensive form. At the same time, the model has several limitations, in particular, dependence on the quality of expert assessments, sensitivity to incomplete or noisy data, and the need for high-speed mechanisms for real-time information processing. Further development of the research involves the creation of a simulation environment for training operators in behavior in crises, automation of strategy generation using intelligent algorithms, and research into the possibility of using reinforcement learning for adaptive optimization of coordination in complex man-made systems. Such an extension will allow for increasing the autonomy of the DSS, reducing dependence on the human factor, and improving the system’s resistance to dynamic threats.
The proposed coordination model has significant potential for use in digital transformation and can be integrated into modern cyber-physical systems, in particular, smart-factory platforms, energy complexes, transport systems, and aviation technological environments. Due to the formalization of the state space, risk assessment mechanisms, and algorithms for coordinating management actions, the model can work with data flows in real time, which makes it compatible with IIoT infrastructure, digital twins, and AI-oriented control systems. Such integration ensures adaptability to dynamic technological changes, increases control autonomy, and allows the model to be efectively applied in industrial automation, energy networks, logistics operations, and critical aviation safety systems, where a high level of coordination and eficiency is a key factor in resilience.
The concept of constructing a model of coordination control in a hierarchical system based on the procedure of aligning strategic goals with the local goals of each stratum has been considered. It has been shown that the procedure for synthesizing coordination strategies will be efective only if the upper level of the management hierarchy is guided by managers with a high level of professionalism and intellectual resilience, since these personal qualities are developed over many years of preparation, whereas crisis situations often have an explosive nature. Consequently, managerial commands may drive the system into an emergency state due to the inability of the upper level to make efective decisions.
The proposed procedures for searching schemes of control problem solutions are based on the coordination of the control object’s state space with the target space, ensuring goal-oriented coordination at all levels of the hierarchical structure of technogenic systems. The evaluation of crisis situations is based on determining the control strategy, identifying the problem situation, and constructing the decision-making procedure.
The generation of control strategies is grounded in the coordination of management processes across all hierarchical levels with respect to the primary control objective (goal orientation) and the problem-solving strategy using the model of coordination strategies for decision-making. Such a mechanism enables DMs to select management and coordination strategies for all control decisions within hierarchical management systems.
The selection of individuals who make decisions in hierarchical technogenic systems is carried out according to the criteria and requirements for the cognitive characteristics of decision-makers to ensure efective crisis management under the influence of threats and attacks. The factors influencing the emergence of crisis-situation risks, examined in this study, as well as the assessment components and their allowable values developed on the basis of testing, make it possible to perform such selection and to control the level of knowledge of information technologies required for implementing the system-management process in crisis situations by the operator as a cognitive agent.
Further research by the authors will be aimed at expanding the presented model by developing a full-fledged practical case for various types of crises in man-made systems, including emergency deviations of technological processes, equipment failures, and cyber threat scenarios. It is planned to create a detailed scenario experiment with a comparison of manual decision-making and the work of the DSS coordinator, which will allow assessing the efectiveness of the model in real conditions and deepening its applied validation.
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