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• CLUSTERING ALGORITHM FOR LARGE GROUPS OF EXPERTS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHOD

  Е.М. Gerasimenko , P.S. Gerasimenko

  6-21

  2025-12-30

  Abstract ▼

  This article presents an algorithm for achieving consensus in social networks during large‑scale group decision‑making with incomplete probabilistic fuzzy information containing elements of uncertainty, which takes into account the trust relationships among experts. A method for clustering experts based on interpretive structural modelling is proposed. It serves both to classify experts and to enhance the efficiency of consensus achievement.The study examines trust propagation and aggregation operators for probabilistic fuzzy information with elements of uncertainty. These operators enable indirect trust assessment and determination of experts’ weight coefficients. As a result, it becomes possible to form several subsets of experts and to determine weight coefficients for a large number of experts based on their mutual trust relationships. Based on the clustering of experts and the calculated indirect trust relationship between experts, decision‑making in emergency situations is carried out by achieving consensus, taking into account fluctuating probabilistic fuzzy information, and the best evacuation alternative is identified.
  The assessments provided by experts in the form of probabilistic fluctuating fuzzy values allow for effective modelling of doubts, uncertainty, and inconsistencies in expert evaluations when a group of experts or various expert organisations are involved. At the same time, it becomes possible to take into account different expert assessment values in multi‑criteria decision‑making tasks when experts cannot agree on common membership degrees. The algorithm allows classifying a large group of experts into several subsets based on their social trust relationships. This method prevents the formation of overlapping subsets and does not require pre‑setting clustering parameters. It relies exclusively on social trust relationships between experts, thereby avoiding the issue of subjective intervention in the clustering process. Compared to traditional clustering methods, the interpretive structural modelling‑based clustering approach effectively reveals the hierarchical structure of relationships among experts. It also minimizes the number of participants in large‑scale group decision‑making within a social network by reducing the dimensionality of the expert set. Clustering experts based on the interpretive structural modelling method significantly enhances the efficiency and feasibility of large‑scale group decision‑making

• HYBRID METHODIC FOR PRACTICAL IMPLEMENTATION OF THE SYSTEM OF DECISION-MAKING ON PRIORITY REGULATION

  S.A. Tkalich

  2022-03-02

  Abstract ▼

  The task of building a decision-making system within the framework of automated systems of
  accident-free control of technological processes based on forecasting models is considered.
  The analysis of models and methods of emergency forecasting is presented. The task of developing
  a methodology for practical implementation of the system based on the integral criterion of accident-
  free control, taking into account the time reserves to bring the process to a normal state
  (emergency forecasting system) and the resource component (preventive maintenance system) is
  formulated. The conclusion is made about the expediency of building decision-making systems and
  automated control systems based on forecasting models, as the most promising approach to solving
  the problem of accident-free control of technological processes. The principle of building a
  decision-making system is based on the use of the integral criterion of accident-free management.
  The block diagram of the algorithm for calculating the integral criterion of accident-free control is
  presented. Hybrid methodology for practical realization of such systems on the basis of priority
  regulation, which includes a standard regulator, is offered. The procedure of formation of priority
  regulators according to the forecast data is described. A block diagram of the algorithm of the
  priority regulator, which determines the critical parameter on the basis of sensitivity theory, is
  presented. In case of a positive forecast on an accident, the critical parameter is selected by the
  maximum of the sensitivity coefficient, and the minimum or maximum value of the parameter depending
  on the sign of its rate of change is fed to the standard regulator from the matrix of critical
  values as a set point. The structure of the decision-making system based on the concept of accident-
  free control of technological processes is given. The station of accident-free control forms the
  data for the decision-making module on the basis of the compositional model of emergency forecasting
  and the integral criterion of accident-free control. Algorithm block diagram of the decision
  making module for priority regulation is given.

• APPLICATION OF FUZZY LOGIC FOR MAKING DECISIONS ABOUT EVACUATION IN CASE OF FLOODING

  Е.М. Gerasimenko, V.V. Kureichik, S.I. Rodzin, A.P. Kukharenko

  2022-11-01

  Abstract ▼

  We are talking about natural disasters, such as flooding, which can be predicted a few
  hours before they occur so that evacuation of the population can be organized. Evacuation
  means that people in disaster areas must leave these areas and reach shelters. The article pr esents
  an analysis of the decision-making process on evacuation, the main criteria determining
  the decision and the main stages of using fuzzy logic to make a decision on evacuation based on
  qualitative and quantitative values of the decision-making criteria. These stages include selection
  of criteria, determination of qualitative input and output variables, fuzzification of variables,
  definition of the base of fuzzy rules, construction of fuzzy inference, visualization of results
  and sensitivity analysis. When modeling, the following criteria were taken into account: the
  predicted flood level, the level of danger, the vulnerability of the area of the expected flood and
  the possibility of safe evacuation. The predicted flood level was based on the parameters of the
  maximum level and the rate of water rise. The hazard level reflected the physical characteristics
  of the flood and its potential impact on the safety of people in the flood area. The vulnerability
  of the area of the expected flood was defined as the inability at the local level to prevent people
  from direct contact with flood waters during the event. The possibility of safe evacuation was
  defined as a set of limitations and potential negative aspects that could delay or hinder the successful
  evacuation. The description of qualitative variable criteria for making a decision on the
  need for evacuation, examples of determining the base of fuzzy rules are presented. The fuzzy
  model is implemented using Matlab Fuzzy Logic Toolbox. The procedure of fuzzy inference and
  interpretation of the solution and a model of several scenarios and flood situations are described.
  The method by which a fuzzy model of decision-making on evacuation can be applied in
  combination with a geoinformation system is considered. The actions related to the need for
  evacuation for various scenarios and circumstances are presented.