Peasant Farms and Industrial Development: Mathematical Approach to Analysis and Planning

Peasant Farms and Industrial Development: Mathematical Approach to Analysis and Planning

Andrey Tuskov (Penza State University, Russia), Viktor Volodin (Penza State University, Russia), Anna Goldina (Penza State University, Russia) and Olga Salnikova (Penza State University, Russia)
DOI: 10.4018/978-1-7998-1581-5.ch008

Abstract

The authors propose a general formulation of the economic and mathematical model of the problem of optimizing the size of the newly created peasant farms and Industrial Development, taking into account the chosen specialization of activity, as well as determining the optimal parameters for the already-known size of farms. The developed mathematical model differs from the classical one by the presence of additional blocks, which prescribe the sales channels of manufactured products and determine the necessary financial resources. The proposed methodological approach should be used for planning the development of regional economies, taking into account the existing specifics.
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Background

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

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.

Linear Programming (LP, also called Linear Optimization): Is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization).

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

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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