2019. No. 1 Vol.13

      This paper discusses real time control of an enterprise. The history of this concept is associated with the arrival of the real time enterprise (RTE) concept in 2002. The RTE concept has been interpreted variously, mainly in the areas of computer science and marketing. With the advent of new digital technologies and digital organizations, the RTE concept has received a new practical application in management.
      This paper discusses an important characteristic of the RTE concept – real time scale and the division value of this scale. The authors have investigated the factors influencing the division value of this scale. The composition of these factors includes not only management, but also digitalization factors. We propose considering the real time scale as a time characteristic of organization adaptation to dynamics, uncertainties and complexities that are present in its environment. In this case, the division value of the real time scale is the time that characterizes the limit after which there is a loss of control in the organization.
      There are two groups of factors influencing the division value of the real time scale: objective factors (for example, the speed of the actual operating processes), and subjective factors (for example, limitations on participants’ knowledge of the real situation and/or their opportunistic behavior). Nevertheless, the real time scale is a real phenomenon which has objective manifestations. In a well managed organization, management always spontaneously reaches a consensus regarding the division value of the real time scale. Meanwhile, the division value of real time scale is the time division value of a real clock which is sufficient for precise planning and control of deviations from the plan.

      In this paper, a new approach to multi-criteria decision making is proposed based on linguistic information taken from a group of autonomous experts. This approach provides an opportunity to better analyze and find solutions for poorly structured problems with consideration of their multidimensionality and uncertainty of context. One of the key components of the proposed methodology is the hierarchy of abstractions proposed by John van Gigch, which presents the levels of alternative solutions and criteria for assessing them. By integrating this hierarchy, it is claimed that the problem situation can be comprehensively analyzed. Therefore, we call our approach multi-level multi-attribute linguistic decision making (ML–MA–LDM).
      Our approach includes a methodology that is the particular sequence of steps and the mathematical model, as well as the method to automatically distribute weights of experts’ assessments depending on their confidence level. Furthermore, this novel approach supports both qualitative and quantitative assessments that are strictly propagated through the complete decision making process across all hierarchical levels of abstraction. Finally, we demonstrate a prototype of a multi-agent expert system for solving poorly structured models with regard to their context uncertainty and multiple aspects. This prototype plays the role of simulation engine for competitive solutions and for verification purposes of the proposed methodology.
      Capabilities of the developed approach and the prototype were demonstrated in a practical case of solving a complex conflict problem of strategic management, as well as rigorous analysis of the proposed approach strengths and weakness that defines the direction for further research.

      This article presents a new approach to designing decision-making systems for socio-economic and ecological planning using parallel real-coded genetic algorithms (RCGAs), aggregated with simulation models by objective functions. A feature of this approach is the use of special agent-processes, which are autonomous genetic algorithms (GAs) acting synchronously in parallel streams and exchanging periodically by the best potential decisions. This allows us to overcome the premature convergence problem in local extremums. In addition, it was shown that the combined use of different crossover and mutation operators significantly improves the time efficiency of RCGAs, as well as the quality of the decisions obtained (proximity to optimum), providing a more diverse population of potential decisions (individuals).
      In this paper, several suggested crossover and mutation operators are used, in particular, a modified simulated binary crossover (MSBX) and scalable uniform mutation operator (SUM), which is based on quantization of the feasible region of the search space (dividing the feasible region on small subranges with equal lengths) while taking into account the common amount of interacting agent-processes and the maximum number of internal iterations of GAs forming potential decisions through selection, crossover and mutation. Such a functional dependence of the parameters of heuristic operators on the corresponding process characteristics, aggregated with the combined probabilistic use of various crossover and mutation operators, makes it possible to get maximum effect from the multi-processes architecture. As a result, thecomputational possibilities of RCGAs for solving large-scale optimization problems (hundreds and thousands of decision variables, multiple objective functions) become dependent only on the physical characteristics of the existingcomputing clusters. This makes it possible to efficiently use supercomputer technologies.
      An important advantage of the proposed system is the implemented integration between the developed parallel RCGA (implemented in C++ and MPI) and the simulation modelling system AnyLogic (Java) using JNI technology. Such an approach allows one to synthesize real world optimization problems in decision-making systems of socio-economic and ecological planning, using simulation methods supported by AnyLogic. The result is an effective solution to single-objective and multi-objective optimization tasks of large dimension, in which the objective functionals are the result of simulation modeling and cannot be obtained analytically.

      Raising the effectiveness of strategic management in conditions of high complexity and dynamic change of modern management systems requires the development of an appropriate mathematical toolkit. The task of raising effectiveness of strategic management is especially topical for heat supply enterprises of the Donetsk region, where operations have been complicated by a number of general system problems, and by the presence of substantial external challenges. At the same time, the question of using mathematical apparatus to raise the effectiveness of strategic management of enterprises in the sphere of residential-communal services appears not to have been widely studied. In this regard, the objective of this study is raising the effectiveness of strategic management of heat supply enterprises of the Donetsk region by developing a respective toolkit of mathematical modeling. To achieve the goal we have set, in this work we carried out an analysis of the viability of the system using the methodology proposed by S. Beer; we made an analysis of the elements of the market of heat supply, and also developed system dynamic models based on the approach of J.W. Forrester.
      As a result of our research, we discovered the basic problems influencing the viability of the system at the strategic level. It was established that the problems revealed are the consequence of the imperfections of the methodological base, including absence of timely information on the dynamics of the external environment, forecasting of the key parameters, a toolkit for making decisions, etc. For the purpose of finding a toolkit to improve the methodological base, we performed an analysis and forecast of the heat supply market in the Donetsk region as part of the external environment which exerts a very significant influence on the activity of the heat supply enterprises of the Donetsk region.
      In the course of this market analysis, we established that the offer of heat supply services is not constant and depends on the tariff setting costs. Due to this, we proposed an approach to forecasting tariff setting costs based on the methodology of A.G. Ivakhnenko but distinguished from that by the presence of a training sample and two test samples. In addition, in the course of analyzing the market we discovered new forms of demand for heat supply services – lost demand and unpaid demand. On the basis of the dependencies established, we built a model for forecasting the behavior of consumers of a heat supply company oriented to the level of marketing. With the help of this model, by means of supplements to it and modifications, we built a complex model of strategic management of heat supply enterprises of the Donetsk region allowing us to analyze the effectiveness of using one or another lever of strategic management on the basis of scenario analysis.

      The article deals with a model for organizing railway transportation on a long stretch of road between two node stations connected by a large number of intermediate stations. Between two arbitrary neighboring stations, there is a railway track for temporary storage of cargo. The movement of cargo is carried out in one direction. To ensure the smooth movement of cargo, two technologies are used which are common for all stations. The first technology is based on the procedure of interaction of a station with both neighboring stations and adjacent railway tracks. The second technology uses the technical capabilities of the station itself and is based on the interaction of the station with neighboring railway tracks. For cargo transportation, a simple control system is used which provides for measuring the volume of transported goods at neighboring stations with a single time lag.
      This work is devoted to describing and studying the dynamics of the number of roads involved in the railway tracks. For this purpose, a system of differential equations is formed, the right parts of which are functions of variables describing the dynamics of the number of roads involved in the stations. The starting point for this study is previously obtained results from studying the dynamics of the number of tracks involved in the stations (a brief description of these results is given in the Introduction). What follows is the description of the dynamics of the number of roads involved in the railway tracks. Possible variants of the dynamics (growth of the number of the roads involved on one railway tracks and falling on others) and their dependence on parameters of the model are investigated. We also study the dependence of the rate of change in the number of involved roads on the railway tracks on the model parameters. We then find the parameter of control by which it is possible to provide arbitrarily small speed of growth (fall) of the number of the roads involved on all railway tracks.

      These days one of the main problems that must be solved to ensure information security in cloud services for corporations as well as for individual clients is to correctly identify and predict hacking in the network traffic. This paper presents statistics on information security threats, provides classification of information security threats for cloud services, identifies hackers’ goals, and proposes countermeasures.
      A vital task is to develop an effective method that could be used to protect cloud services from various network threats, as well as to analyze the network traffic. For these purposes, we chose a method based on an additive time series model, which allows us to predict the undesirable network traffic. To test this method, we obtained quantitative parameters for the undesirable traffic by simulating a network attack and collecting empirical data that describe this process. We used special software that simulates a network attack, and software that records and processes all the empirical data needed for the research.
      Using the data obtained, we analyzed the efficiency of the method based on the additive time series model. We demonstrated that this method is also applicable for research into the general dynamics of the number of network attacks in cyberspace. This method also allows us to reveal how the dynamics of the number of hacker network attacks depends on season, date, or time. The results show that, based on data describing the network traffic, one can identify and predict the undesirable hacker threats.