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FEATURES OF SUPPORTING DECISION MAKING IN MODERN ENTERPRISE INFOCOMMUNICATION SYSTEMS
Sergey V. Pavlov, MTUCI, Moscow, Russia, h.108@yandex.ru
Vladimir A. Dokuchaev, MTUCI, Moscow, Russia, v.dok@tlsf.ru
Victoria V. Maklachkova, MTUCI, Moscow, Russia,
Information about authors:
Sergey V. Pavlov, PhD (Tech), Associate Professor, Associate Professor of the Department "Multimedia Communication Networks and Services" MTUCI, Moscow, Russia
Vladimir A. Dokuchaev, DSc (Tech), Professor, Head of the Department "Multimedia Communication Networks and Services" MTUCI, Moscow, Russia
Victoria V. Maklachkova, Senior Lecturer of the Department "Multimedia Communication Networks and Services" MTUCI, Moscow, Russia Sergey S. Mytenkov, Postgraduate Student of the Department "Multimedia Communication Networks and Services" MTUCI, Moscow, Russia
Для цитирования:
Павлов С.В., Докучаев В.А., Маклачкова В.В., Мытенков С.С. Особенности поддержки принятия решений в современных корпоративных инфокоммуникационных системах // T-Comm: Телекоммуникации и транспорт. 2019. Том 13. №3. С. 71-74.
For citation:
Pavlov S.V., Dokuchaev V.A., Maklachkova V.V., Mytenkov S.S. (2019). Features of supporting decision making in modern enterprise infocom-munication systems. T-Comm, vol. 13, no.3, pр. 71-74.
DOI 10.24411/2072-8735-2018-10252
v.maklachkova@tlsf.ru
Sergey S. Mytenkov, MTUCI, Moscow, Russia, mitenkovss@rspp.ru
Keywords: Decision Maker, Information and Analytical Activities, Decision Support System, Model, Technical System, optimal technical solution, Enterprise Information and Communication Management Systems.
The purpose of this work is to consider the method of automatic selection of the optimal technical solution for Modern Enterprise Information and Communication Management Systems of Technological and Business Processes. These solutions based on a generalized spectral representation of the characteristics of the used Technical Systems (TS) and the presentation of methodological approaches to the analysis of the technical characteristics of the considered TS and tools. The concept of the DSS is based on the optimization problem - to find such a management decision for a Decision Maker (DM) that would have the maximum positive effect in the presence of a number of restrictions.The proposed spectral method of automatic selection of the optimal technical solution for Modern Enterprise Information and Communication Systems allows to automate the process of evaluating the effectiveness and choosing the optimal technical system. The same methodical approach can be used in the framework of Decision Support Systems to build a model for the selection of forces and means for solving a problem.
The system analysis technique is developed and applied if the Decision Maker (DM) does not have the necessary information about a particular situation, allowing it to be formalized and find a solution to the problem. The basis of Decision Making management is a specific type of intellectual activity - Information and Analytical Activities (IAD), as a set of measures for searching, collecting, processing information, evaluating it, analyzing and predicting the state of the management object.
The importance of IAD in modern conditions increases dramatically due to the following reasons:
• a sharp increase in the volume of information processed during decision-making 011 information management;
• the difficulty of identifying and establishing causal relationships in the information on the existing area of management;
• the complexity of automating processes in modern enterprise information and communication systems for managing technological and business processes.
This necessitates the creation of a specific part of the modern enterprise information and communication system - a Decision Support System (DSS),
The concept of the DSS is based on the following optimization problem - to find a management solution for decision makers that would have the maximum positive effect in the presence of a number of constraints.
At the planning stage, ihe decision maker must perform Ihe following actions;
1. To collect information on the availability, characteristics, lechnical condition and other characteristics of the Technical System (TS) at its disposal.
2. Evaluate the capabilities of each TS for solving the task of control and select it,
3. To evaluate ihe influence of external influencing factors on the result of the management process.
At the stage of solving the decision maker it is necessary:
1. Assess ihe results of applying the selected TS to the management process.
2. To evaluate the influence of external influencing factors 011 the result of the management process.
Consider the fundamental features of (he application of DSS on the example of solving the problem of planning the management process.
As mentioned above, the basis of the DSS is the method of optimization of the objective control function. As such a function al this stage is the degree of ensuring the maximum level of ihe task, i.e. choice of TS, the most appropriate solution to the task. The solution is divided into several consecutive steps. Suppose (here is a set ofTS in the amount of i samples, each of which has j-Lechnical characteristics.
Stage I, All options for the use of existing TS are evaluated with the preparation of a general tabic of all technical characteristics for their subsequent ranking.
Stage 2, It is necessary to carry out formalization (coding) of technical characteristics of the TS in order to ensure their comparison in automatic mode, ll is advisable lo group the characteristics into 3 groups:
1. The group with the preferred maximum value;
2. The group with the preferred minimum value;
3. A group of qualitative (linguistic) characteristics.
Specifications must be presented in the form of normalized
numerical values, which will allow the use of mathematical pro-
A,, JC,.
Xn *// %
Xmj A" nin
cessing methods. Depending on the group characteristics are presented in the form:
for the first group A-, = / max[.v, ],
for the second group Xj/ -aj minl.vj/.v, , where: X.j - normalized and weighted values of the j-th characteristic of the TS;
X. - the absolute values of the j-th characteristic of the TS; a. — the weighting factor of the j-th characteristic of the TS. The characteristics of the third group A',-, can be assigned
numerical values of 1,0, if the characteristic has a positive effect on the solution of the problem; 0.1, if the characteristic affects ihe solution of the problem negatively; 0.5, if the characteristic does not affect the solution of the problem, ¡n the case where the influence of the characteristic can be estimated more accurately, more quantitative estimates can be entered.
Thus, at stage 2, a matrix as describing the characteristics of X of existing TS is formed in Ihe form:
Xu =
The introduction of the coefficieni a in determining the values of the elements of the matrix XtJ is caused by the ambiguity
of the influence of the values of individual characteristics of the TS on the solution of various control problems. Accounting for this feature allows us to take into account as significant the various characteristics of the TS for different control tasks.
Thus, the matrix X~ contains the elements x. , which describe the degree of proximity of each characteristic of the TS lo the possible best value for the existing set of TS, that is, the matrix describes the quality of the existing TS w ith respect lo the best value. When using the spectral assessment method, it is impossible to obtain the absolute values of the characteristics of the quality of the TS, and it is possible to obtain only a comparative assessment of the characteristics of each of the available TS samples. Obtaining such an assessment in automatic mode is possible by various methods. In this paper, the computationally simple iteration method will be applied.
At stage 3, using the iteration method, it is necessary to ob-lain the numerical values of the quality of each TS based 011 the existing matrix of weighted normalized characteristics Xu to
solve the stated control problem and, based 011 these data, build a ranked sequence of the existing TS.
The essence of the method consists in the iterative determination of the coefficients b. and m according to the following
formulas:
b. = x.
where:
b. - a relative (to the maximum for a given TS set) measure of the quality of the i-lh TS specimen;
T-Comm Tom 13. #3-2019
m. - a relative (to the maximum for a given TS set) measure
Of the influence of the j-th characteristic of the TS on its measure of quality.
The numerical values of bt and m arc obtained by successive approximations. As a zero approximation, the assignment of all h and m- to a value equal to 1 is used. Since the numerical values of the coefficients bi and m .themselves arc insignificant, and only
the relationship between them is important, before the next iteration it is advisable to normalize them to maximum values.
As an example of the application of the method, we consider a comparative assessment of the systems SI, S2, S3, S4 and S5, characterized by the parameters K.1 - K.8. Let the initial Table I contain the parameters X, normalized for each criterion for each system.
Table I
K1 K2 K3 K4 K5 K6 K7 K8
S1 0.510 0 510 0,876 0.216 0.600 1.0C0 0,833 1.000
S2 0,917 0.875 0,600 0.871 0,429 0,867 0,833 0.400
S3 1,000 1,000 0,489 1.000 1,000 0,867 0,808 0.300
S4 0,458 0,368 0,686 0.110 0,600 0,800 0,833 0.900
S5 0,508 0,675 1,000 0.169 0,750 0.867 1,000 1.000
Let, on the basis of expert estimates, the following values of the weight coefficients of the Kî - K8 system parameters are obtained: a, = 0.22, a2 = 0.194, a. = 0.167, a4 = 0.139,
Oj =0.111, = 0.083, a7 = 0.055, ^ - 0.028. Thus, the parameters K ] - KR in the source table are arranged in descending order of influence on the solution of the control problem. At the same time, the order and weight of the parameters can be changed as the conditions of the task change, since for new conditions the influence of the parameters may change. Then, taking into account the values of the coefficients à , the values of the
normalized parameters xy of the systems are converted into the values presented in Table 2.
Table 2
K1 K2 K3 K4 K5 K6 K7 K8
S1 0,112 0.100 0,145 0.030 0,067 0.083 0,046 0.028
S2 0,202 0.170 0,100 0.121 0,048 0.072 0,046 0,011
S3 0,220 0.194 0,082 0.139 0,111 0.072 0,044 0.008
S4 0,101 0,071 0,115 0.015 0,067 0.066 0,046 0.025
S5 0,112 0,131 0,167 0.023 0.083 0,072 0,055 0.028
The calculation using the iteration method described above allows to obtain estimated coefficients Ij. and m . In accordance
with the existing task of management, it is necessary to choose the TS, the most suitable for solving this problem. Obviously, an indicator that allows such a choice to be made is the indicator bi, representing the measure of quality of the i-th sample of the vehicle relative to the maximum for a given TS set. Since the values of the coefficient m are of an official nature and are needed
only to obtain the values ofthc indicator b. of the next iteration step, Table 3 presents the values of only the parameter b .
It is advisable to carry out the iteration process until the value ofthc indicator b ccascs to change significantly at caeh iteration
step, i.e., while bf +1 . The TS is selected according to the maximum value of the indicator i at the stage of stopping the computation process.
Table 3
ba A, h b, à
S1 1,000 0,611 0,425 0,412 0,410
S2 1,000 0,770 0,564 0,558 0,556
S3 1,000 0,B70 0,627 0,622 0,620
S4 1,000 0.506 0,349 0,338 0,336
S5 1,000 0,671 0,473 0,459 0,456
As can be seen from the data of Table 3, the values of indicators b- at stage 4 of iterations practically coincide with the corresponding indicators at stage 3 of iterations, which indicates the achievement of a given accuracy in obtaining data of a comparative evaluation of systems. The maximum value of the coefficient b is located in the third row ofthc table and is shown in
bold for clarity.
Thus, according to the results of ranking calculations, it is advisable to choose the S3 system for solving the problem. The heuristic evaluation of systems based on the data in Table I leads to a similar result, since the S3 system is characterized by the presence of maximum indicators in columns Kl, K2, K4 and K5 ofthc table, which corresponds to the best possible value from the entire set of possible TS.
The obtained evaluation ofthc effectiveness of the systems allows, in addition to the system with maximum efficiency, to determine the next system in terms of efficiency (in our case, this is the S2 system) and cut off the inefficient S4 and SI systems.
Conclusion
The spectral method of automatic selection of the optimal technical solution for Modern Enterprise Information and Communication Management Systems considered in the article allows to automate the process of evaluating the effectiveness and choosing the optimal technical system. The same methodical approach can be used in the framework of the DSS to build a model for the selection of forces and means for solving a problem.
References
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3. Bellman R„ Zade L. (1976). Decision Making in Vague Condition. In Coll.: Analysis and Decision Making Procedures, Moscow: Mir, pp. 172-215.
4. Christopher J. Date. (2000). Critic Contribution to the Field of Database Technology. Addison Wesley Longman.
5. Pavlov S.V., Suprunov V.I. (1998). A Method of Pulsed Klectro-tnagnetie Piekup System, Electrical Technology Of Russia, No. 4.
6. Dokuchaev V.A., Mytenkov S.S., Pavlov S.V. (201S). Decision support system for special purpose management system. Proceedings of the International Scientific and Technical Conference "Telecommunication and Computer Systems - 20 IS", pp. 77-79.
7. Dokuchaev V.A., Pavlov S.V. (2018). Methodological foundations for building a model of radio monitoring equipment. T-Comm, vol. 12, no.7, pp. 48-51.
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ОСОБЕННОСТИ ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ В СОВРЕМЕННЫХ КОРПОРАТИВНЫХ ИНФОКОММУНИКАЦИОННЫХ СИСТЕМАХ
Павлов Сергей Владимирович, МТУСИ, Москва, Россия, h.108@yandex.ru Докучаев Владимир Анатольевич, МТУСИ, Москва, Россия, v.dok@tlsf.ru Маклачкова Виктория Валентиновна, МТУСИ, Москва, Россия, v.maklachkova@tlsf.ru Мытенков Сергей Сергеевич, МТУСИ, Москва, Россия, mitenkovss@rspp.ru
Аннотация
Целью данной работы является рассмотрение метода автоматического выбора оптимального технического решения для современных корпоративных информационно-коммуникационных систем управления технологическими и бизнес-процессами. Эти решения основаны на обобщенном спектральном представлении характеристик используемых технических систем (ТС) и изложении методологических подходов к анализу технических характеристик рассматриваемых ТС и средств. В основе концепции системы поддержки принятия решений лежит оптимизационная задача - найти такое управленческое решение для лица принимающего решения (ЛПР), которое обладало бы максимальным положительным эффектом в условиях наличия ряда ограничений. Предлагаемый спектральный метод автоматического выбора оптимального технического решения для современных корпоративных информационно-коммуникационных систем позволяет автоматизировать процесс оценки эффективности и выбора оптимальной технической системы. Этот же методический подход можно использовать в рамках систем поддержки принятия решений для построения модели выбора сил и средств для решения задачи.
Ключевые слова: лицо принимающее решения, информационно-аналитическая деятельность, система поддержки принятия решений, модель, техническая система, оптимальное техническое решение, корпоративные информационно-коммуникационные системы управления.
Литература
1. Автоматизация поискового конструирования / Под. ред. Половинкина А.И. М.: Радио и связь, 1981. 343 с.
2. Докучаев В.А., Клаповский Н.В. К вопросу о порядке применения генератора радиошума в качестве средства защиты информации. Информационные телекоммуникационные сети (Казахстан), №3-4, 2013. С. 32.
3. Беллман Р., Заде Л. Принятие решений в расплывчатых условиях. В сб.: Вопросы анализа и процедуры принятия решений. М.: Мир, 1976. С. 172-215.
4. Christopher J. Date. Critic Contribution to the Field of Database Technology. Addison Wesley Longman, 2000.
5. Pavlov S.V., Suprunov V.I. A Method of Pulsed Electromagnetic Pickup System // Electrical Technology of Russia, No. 4, 1998.
6. Докучаев В.А., Мытенков С.С., Павлов С.В. Система поддержки принятия решений для системы управления специального назначения. Труды международной научно-технической конференции "Телекоммуникационные и вычислительные системы -2018". С. 77-79.
7. Dokuchaev V.A., Pavlov S.V. Methodological foundations for building a model of radio monitoring equipment. T-Comm, vol. 12, no.7, рр. 48-51.
Информация об авторах:
Павлов Сергей Владимирович, к.т.н., доцент МТУСИ, Москва, Россия
Докучаев Владимир Анатольевич, д.т.н., профессор, зав. кафедрой МСиУС МТУСИ, Москва, Россия Маклачкова ВикторияВалентиновна, старший преподаватель МТУСИ, Москва, Россия Мытенков Сергей Сергеевич, аспирант МТУСИ, Москва, Россия
