Укрупненная модель эколого-экономической системы на примере Республики Армения
This article presents an integrated dynamic model of eco-economic system of the Republic of Armenia (RA). This model is constructed using system dynamics methods, which allow to consider the major feedback related to key characteristics of eco-economic system. Such model is a two-objective optimization problem where as target functions the level of air pollution and gross profit of national economy are considered. The air pollution is minimized due to modernization of stationary and mobile sources of pollution at simultaneous maximization of gross profit of national economy. At the same time considered eco-economic system is characterized by the presence of internal constraints that must be accounted at acceptance of strategic decisions. As a result, we proposed a systematic approach that allows forming sustainable solutions for the development of the production sector of RA while minimizing the impact on the environment. With the proposed approach, in
particular, we can form a plan for optimal enterprise modernization and predict long-term dynamics of harmful emissions into the atmosphere.
This research work deals with the problem formulation of control of complex organizational structures. The mechanism of functioning of such systems is described by example of a vertically integrated company (VIC). The problems of strategic and operative control of VIC are considered. The methods for solving such problems based on genetic algorithms and neural networks are suggested. A new iterative procedure for coordination of strategic and operative control goals based on the estimation of imbalance between shareholder value and net profit distributed for payment of dividends to shareholders is suggested.
The considered system is a double criterion optimization problem with complex multiparameter restrictions.
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.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.
In this work is presented a new approach to the designing of intelligent systems of the control of the shareholder value for the vertical-integrated Financial Corporation (VIFK). Developed system based on using of system-dynamics methods for the simulation of the synergic interaction between different business directions of VIFK for the target of shareholder value maximization. Note, the described system has been successfully introduced in biggest Russian banking groups and it is used for the preparing of strategic decisions.
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.
Authors provide the substantiation of logistic profitability indicator introduction for problem-solving concern the evaluation of logistic system performance, incl. inventory management system.
Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.
A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.