Scientific Journal of KubSAU

In this article, the general structure of technologically complete production chain of the integrated production system of agroindustrial complex was considered. Three different variants of creation of the production chains are described. The main distinctions in activity of chains consist in a source of raw materials. Any technologically full vertical chain includes three stages – production of raw materials, storage and processing, realization. Each of stages is characterized by different situations of the risk. Stages of process of functioning of a production chain were considered in this article. Also, the qualitative analysis of risk for all stages is made and the results of this analysis are considered. Results of this analysis became a basis for improvement of stream model for determination of efficiency of the technological chain taking into account a risk component. In the article, the algorithm of an assessment of efficiency of the technological chain of the integrated production system taking into account a risk component at the stage of creation of a material stream is also described

The article describes the process of developing the two-level technique for assessment of the missed benefit during the landing and cultivation of perennial plantings. The technique is developed for the enterprises where the cycle of production of raw materials breaks into several periods until the first harvest. The choice problem of the most suitable grades for the landing is relevant for such enterprises. The first level of the two-level technique assumes assessment of qualitative and quantitative characteristics of grades with use of such tools as the theory of fuzzy sets and the decisions tree. The results of the first level are entrance data for the second level where there is final assessment of a grade and determination of the missed benefit rather potentially possible at the choice of the recommended grade. In the article also designated the importance of the raw materials stage for the technological chain. It is shown that, being consecutive structure, the technological chain strongly depends on the first stage – a stage of raw materials production. Minimization of the risk situations at the first stage promotes strengthening of the general risk tolerance of a technological chain

To increase the validity of conclusions about the impact of environment on quality of life we need to move from generalities to the application of quantitative modeling techniques. This requires the joint processing environmental databases and databases depicting various aspects of quality of life. These databases need to be handled not just together, but in a comparable form approach, technology and methodology; and we need to implement them in one software system. For the first time in the environmental studies it has been planned to be done with the application of the ASK- analysis and the system called "Eidos". In this work, we set the goals and the objectives of the application of the ASK-analysis to study the effect of environmental factors on the quality of life of the population of the region. The article reveals the urgency of this study; the requirements for the method of conducting the study, the choice of research method, the contents of the objectives of the study. The proposed work is at the edge of mathematical ecology and mathematical modeling of quality of life (which refers to mathematical and instrumental methods of Economics), resulting from expected synergies, consists in obtaining of new knowledge in these fields, that is relevant to both ecology and economy. This knowledge will make it more meaningful and justified for the application of environmental criteria and concepts in the economy

In this article the model and a calculation procedure of an integrated indicator of risk of the internal environment of the integrated production system are described. Then the steps of creation of triangular fuzzy number for expected value of profit are given. At creation of this fuzzy number the concept of an indicator of risk of the internal environment of the integrated production system was used. Further on the basis of the developed model of internal risk the interval model of efficiency of the integrated production system is developed and described. In this article the structure of a production chain of the integrated production system of agroindustrial complex was considered. In technologically complete production chain, as a rule, allocate three stages – raw materials production, storage and conversion, sale of finished goods. Each subsequent stage depends from previous stage, and at each stage various situations of risk are shown. Also the process of risk identification of the internal environment of the integrated production system was considered in this article. Process decomposition is executed, the description of subprocesses is given. For a risk assessment of the internal environment it is necessary to know quantity of production chains, and also to calculate value of risk for each production chain on the enclosed algorithm. Also in the article the principle of recognition and interpretation of results of calculation of an integrated indicator of risk of the internal environment of the agro-industrial integrated production system on the basis of Harrington's scale and standard indistinct 01 classificators is shown

In the article the results of research of efficiency of three types of bread producing production associa-tions of consumers' co-operative society with vertic-al integration and models of optimization of their entrance parameters are resulted

The performance indicators of a trading company in physical and monetary terms is significantly affected by the types and volumes of purchased and sold products, and which she purchased suppliers and the consumers sold. However, the solution to the problem of choosing the rational range of products faces considerable cost of computational and human resources, and lack of baseline data, and in real dimensions this problem has no solution. The paper proposes such a solution is very economical in costs of different types of resources based on the application of information theory, cognitive and control theory

Management problem of agro industrial holding is formulated in the article in general, from one hand, it is necessary to work out recommendations and adaptive model on holding management for it, and from other hand, designing of its model is difficult because of high complexity and dynamics of inner logistics of an management object, its territorially distributed and multi branch character, large amount of economic indexes, characterizing its activity on different levels of its organization. General method of formulated problem decision by means of systemic-cognitive approach is offered. First stage of model synthesis is described: cognitive structure formation of private models, entering its multi-level semantic information model.

Methodology of using systemic cognitive analysis for building multi-level semantic information model of agro-industrial holding management is formulated in the article in general. Based on this, solutions of forecasting problems and support of decision-making process of management and scientific researches are listed

In this article, in accordance with the methodology of the Automated system-cognitive analysis (ASCanalysis), we examine the implementation of the 3rd ASC-analysis: synthesis and verification of forecasting models of development of diversified agro-industrial corporations. In this step, we have synthesis and verification of 3 statistical and 7 system-cognitive models: ABS – matrix of the absolute frequencies, PRC1 and PRC2 – matrix of the conditional and unconditional distributions, INF1 and INF2 private criterion: the amount of knowledge based on A. Kharkevich, INF3 – private criterion: the Chi-square test: difference between the actual and the theoretically expected absolute frequencies INF4 and INF5 – private criterion: ROI - Return On Investment, INF6 and INF7 – private criterion: the difference between conditional and unconditional probability (coefficient of relationship). The reliability of the created models was assessed in accordance with the proposed metric is similar to the known F-test, but does not involve the performance of normal distribution, linearity of the object modeling, the independence and additivity acting factors. The accuracy of the obtained models was high enough to resolve the subsequent problems of identification, forecasting and decision making, as well as studies of the modeled object by studying its model, scheduled for consideration in future articles

In this article, in accordance with the methodology of the Automated system-cognitive analysis (ASCanalysis), we examine the implementation of the 1st and 2nd stages of ASC-analysis: cognitive structuring and formalization of the subject area. At the stage of cognitive structurization of subject area, researchers decide what to consider as the object of modeling, the factors affecting it and the results of their actions. In accordance with the results of the cognitive structurization, we prepare the initial database for the study (training sample or case-based reasoning). At the stage of formalization of the subject area, the base of the original data is being normalized, i.e., we develop classification and description: the scale and graduations and with their use the base of the source data is being encoded. The result is a database of events (eventological database) and the training sample. The stage of cognitive structuring and preparation of the source data is not formalized and the formalization of the subject area is fully automated and performed directly with the use of the universal cognitive analytical system named "Eidos", which is a software Toolkit for ASC-analysis. Stages of cognitive structurization and formalization of the subject area of ASC-analysis are the first steps of data conversion into information and into knowledge. Subsequent steps: the synthesis and verification of system-cognitive model, the decision of problems of identification, forecasting and decision making, as well as studies of the modeled object by studying its model will be considered in future articles