NDLI: Agriculture Production Planning Using a Hybrid Simulation and Genetic Algorithm Approach

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Agriculture Production Planning Using a Hybrid Simulation and Genetic Algorithm Approach

Content Provider	Semantic Scholar
Author	Lireza Atemeh Ehrooz
Copyright Year	2014
Abstract	One of the oldest and most powerful optimizing methods is mathematical programming which used as a powerful tool in decision making. In spite of widespread use of these models, these models are not flawless. Simplifying hypothesizes in approaches like Minimization of Total Absolute Deviation (MOTAD) and Expected Value Variance (EV) reduce the reliability of proposed programs. The hybrid of Genetic Algorithm (GA) and Monte Carlo Simulation (MCS) improve decisions by recognizing the best accidental processes of production planning. The goal of this research is to determine the optimal cropping pattern of agricultural production in Mashhad plain. Data were gathered from Agricultural Jihad Organization of Khorasan Razavi province of Iran including time series of main seven crops in Mashhad plain from 1982 to 2007. Simulating the program was done by using The Decision Tools Suite ver 4.5.2 and improving accidental processes were attained by MATLAB. The linear and quadratic risky programming model were also attained and solved by using WINQSB software. Finally, the results of hybrid model were compared with the result of quadratic risky and linear programming model. Given the stochastic processes without any presumption, the results are more reliable and more realistic. Therefore, having the required information such as the time series of variables, this hybrid model can efficiently suggest the best planning production.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.agricommun.com/attachments/Vol.2_Issue%203/Agri.%20Commun.%202_3_7.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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