Имитационное моделирование стратегического развития социально-экономических систем: поиск эффективных модельных конструкций
This work focuses on the development of system dynamic credit risk model of the company “Bashneft”, which is a major representative of oil refining and oil producing industries.
The author intends to explore possibility of using system dynamics to build models describing production process and financial condition for a company which deals with petroleum refining industry sector. Special attention is paid to how the behavior of such macroeconomic factors as oil prices and oil products prices (on global and Russian markets), US dollar rate, MosPrime rate (this is the National Foreign Exchange Association (NFEA) fixing of reference rate based on the offer rates of Russian Ruble deposits as quoted by Contributor Banks) and tax system (mineral extraction tax, export duties, petroleum products domestic excise tax) influence on the company.
The report discusses the use of National Instruments tools for dependability prediction of electronic devices by simulation modeling. The description of the laboratory bench allowing to develop formal models based on reliability block diagrams, to carry out simulation experiment and to process statistical modeling results, is given as well as an example of this bench usage for reliability prediction of power supply of the lightweight spacecraft.
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, the computational 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 existing computing 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 singleobjective and multi-objective optimization tasks of large dimension, in which the objective functionals are the result of simulation modeling and cannot be obtained analytically.
Enterprises of Russia’s radio industry engaged in development and production of electronic instrumentation (EI) for space vehicles (SV), face problems of insuring reliability, and, first of all, problems of failure-free operation. Failures during EI acceptance tests and accidents at SV operation are real evidence of mentioned problems. One of the reasons of such situation is application of out-of-date and inaccurate methods of estimating the reliability of SV EI at the design stage where developers embed the reliability that will be realized during production and supported at the operation stage.
On the other hand, use of the "lower" estimates of failure-free operation parameters can lead to decrease of SV EI competitiveness, as this way in order to enhance reliability, manufactures unreasonably use various additional ways that lead to deterioration of economic, mass-dimensional and other indices. Therefore, increase of accuracy of estimating the reliability of SV EI with long terms of active existence is pressing problem, in particular for EI wherein redundancy as well as reconfiguration is used to ensure reliability.
This article presents the developed decision support system for the rational management of landscaping on the example of Yerevan, Republic of Armenia. Using agent-based modeling methods, a simulation model has been developed for the distribution of emissions of harmful substances into the atmosphere, taking into account their interaction with green plantings (trees) in order to minimize the concentration of harmful emissions in protected (socially significant) areas, in particular, in kindergarten areas. An important element of the developed system is the proposed genetic algorithm for real coding, aggregated with a simulation model of emission propagation, implemented on the AnyLogic platform. As a result of numerical experiments, the best configuration of tree planting around kindergartens in Yerevan has been obtained, ensuring the minimum level of concentration of harmful substances in the respective protected areas, taking into account the restrictions on the cost of the greening program. The developed software complex allows for: further detailing of the emission distribution simulation model; modeling strategies for vertical gardening (i.e., landscaping walls and roofs); clarification of the methodology for predicting the dynamics of the spread of harmful emissions; development of the software complex as a decision support system for environmental planning, taking into account the density of residents distribution, and remoteness of urban buildings from industrial enterprises and traffic flows (roads) in the formation of an optimal greening strategy.
The goal of our research is to investigate how the communication structure of an organization aﬀects its performance. In the paper, we study a simulation model of a self-organizing team conducting scientiﬁc research. The key parameter of the model is the social graph of the organization, which deﬁnes the team creation process. For this model, we formally deﬁne the average utilization rate of the group. Under some natural condition, the utilization rate is a function of the social graph. Lower and upper bounds of this characteristic are established. The obtained result has evident practical meaning and policy implications for organization management.
The paper focuses on the questions of analysis, selection and monitoring of management systems development programs, relying on comparison of the program related expenditures and dynamics of the system’s maturity level, using simulation modeling. In this regard, integrated indicators, which characterize effectiveness of the development program, its financial aspects, as well as its efficiency and duration, are considered. Specific features related with of calculation of the development program integrated indicators using the results of simulation modeling and appropriate statistical metrics are disclosed.
This work discusses a possibility to assess the probability of company default using system dynamic model. This approach is based on Monte Carlo Simulation with various inputs for a system dynamic model. The results are compared with the estimations of rating agencies.