Optimizing the Production Parameters of Peasant Holdings for Industrial Development in the Digitalization Era

Optimizing the Production Parameters of Peasant Holdings for Industrial Development in the Digitalization Era

Andrey Tuskov, Anna Goldina, Olga Luzgina, Olga Salnikova
DOI: 10.4018/978-1-7998-1581-5.ch007
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One of the determining factors for ensuring regional food security is the sectoral structure of production. It determines the specialization and combination of industries, on which the degree of tension, balance, and economic efficiency of the production program of peasant farming depends. This is achieved subject to the proportionality of the elements of the sectoral complex. For this, it is necessary to coordinate production volumes with available resources, the level of intensification of crop production and animal husbandry, the size of crops, individual crops, and livestock, etc. The size of peasant farms and their structure (the composition and area of land, the combination and size of main and additional industries, the structure of crops) depend on many natural and economic factors. There are various options for the organization of production and territory for the same farming with certain resources of land, labor, and capital. The main task is to choose the optimal one that corresponds to the interests of the farmer and gives the maximum economic effect.
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Literature Review

Linear programming is methods that solve the problem of distributing limited resources between competing activities in order to maximize or minimize some numerical values, such as marginal profit or expenses. The beginning of modern stage in development of economic and mathematical modeling was laid by the academician A. N. Kolmogorov. He conducted modeling of economic phenomena and processes and developing of data analysis methods in traditions of the Soviet probabilistic and statistical scientific school. In 1946, A. N. Kolmogorov gave a geometric presentation of least squares method (Kolmogorov, 1946).

General problems of mathematical modeling of economic phenomena and systems are considered in the monographs of N.P. Buslenko (Buslenko, 1978), J. Kemeny and J. Snell (Kemeny and Snell, 1970), J. von Neumann and O. Morgenstern (Von Neumann and Morgenstern,1944) and others.

Today linear programming at all and least squares method in particular is popular not only in practical research, it is used to manage and value early or multiple exercise real options. In business, it can be used in areas such as production planning to maximize profits, selecting components to minimize costs, selecting an investment portfolio to maximize profitability, optimizing the transport of goods to reduce distances, distributing staff to maximize work efficiency and scheduling work in in order to save time. But also it can be used in theoretical researches. A. Ahn and M. Haugh have researched using of linear programming in control of diffusion processes (Ahn and Haugh, 2015). S. Nadarajah and N. Secomandi have studied relationship between least squares Monte Carlo and approximate linear programming. Their research in this area has started applying approximate linear programming and its relaxations, which aim at addressing a possible linear programming drawback (Nadarajah and Secomandi, 2017).

Key Terms in this Chapter

Peasant Farming: Refers to a type of small-scale agriculture.

Endogenous Variable: A variable in a statistical model that’s changed or determined by its relationship with other variables within the model. Endogenous factors are the opposite of exogenous variables, which are independent variables or outside forces.

Optimization Methods: Often non-linear, non-convex, multimodal, and multidimensional, and might be expressed by both discrete and continuous variables, which makes this a difficult problem.

Exogenous Variable: Is used for setting arbitrary external conditions, and not in achieving a more realistic model behavior. An exogenous variable is a variable that is not affected by other variables in the system.

Multidimensional Data Model: Is designed to solve complex queries in real time. The multidimensional data model is composed of logical cubes, measures, dimensions, hierarchies, levels, and attributes. The simplicity of the model is inherent because it defines objects that represent real-world business entities.

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