Имитационное моделирование и инструментарий принятия решений в процедурах формирования сценариев развития промышленной территории
This article describes the tools for analysis scientific-industrial complex of the city, analysis of scenarios of development of industrial areas, sets out the basic parameters of a mathematical model of the balance of interests.
The purpose of developing a cognitive model has been defined as the construction and analysis of simulation models improve interaction between government and business. In line with this objective has been hypothesized that an increase in the efficiency of interaction between business and government increased the values of competition in politics and economics, which in turn are directly related to each other. The latter is not in doubt, since the state of competition in the economy is inextricably linked to the legislative machinery of antitrust restrictions, by which representative bodies suppress or support unfair competition.
Basing on the data of migrant population surplus/decline in Russian cities for the period 1991-2009 the attempt is made to evaluate the impact of the population size of a city as well as the city position in the system of central-peripheral relations on its migration balance. The author also explains the existing migration mobility pattern through hierarchy of cities within a region.
Authors provide the substantiation of logistic profitability indicator introduction for problem-solving concern the evaluation of logistic system performance, incl. inventory management system.
Subject Pursuing the socio-economic policy in regions requires understanding the processes of concentration of resources, population, enterprises in certain territories, mostly, in cities. Recent studies show increasing interest of economists in the Zipf's Law manifestation in the regional system, and cities distribution under the rank-size principle.
Objectives The aims are to test the Zipf's Law in Russian cities, to support or reject the hypothesis that in Russia the Zipf coefficient depends on the size of the geographical territory of the federal district.
Methods We used the least square method to analyze the Zipf's Law in Russian cities in general, and in each federal district, in particular. The sampling includes 1,123 Russian cities with population over 1,000 people in 2014. Results The Zipf's Law manifests in the entire territory of the Russian Federation. In federal districts, the Zipf coefficient ranges from -0.65 (the Far Eastern Federal District) to -0.9 (the Ural and North Caucasian Federal Districts). The analysis of the sampling of cities with population over 100 thousand people demonstrated -1.13 Zipf’s coefficient.
Conclusions The test of the Zipf's Law for Russian cities shows that it is valid for small (8,600-15,300 people) and large cities (66,700-331,000 people). The Zipf's Law fails for cities with population exceeding one million people (except for the city of St. Petersburg). The study supports the hypothesis on dependence of the Zipf coefficient on the size of a federal district.
The importance of strategic management today is unquestionable. However, when strategizing the organization is often regarded as a single whole, differences in aims and areas of operation of its parts not being considered. This approach works for many organizations, but in the case of a distributed structure its parts may function in the markets which have different requirements, competition intensity and qualification of consumers. Besides, the departments of that organization may have different levels of development. In our present work we do not consider the whole range of distributed organizations, but concentrate on universities, as they have common characteristics with commercial organizations and, at the same time, are very specific in their rules and areas of development. We focus on developing a new modeling method for decision support while designing a balanced hierarchical strategy for distributed universities. This implies beginning from the strategy for the whole organization and moving on to development of individual strategies for its departments. Thus, the proposed method contains two parts: a sub-method to develop departmental strategies and a sub-method to calculate interaction among departments.
This article describes the proposed structure and semantics of the model which can be used in the both of sub-methods.