A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis
International Journal on Data Science and Technology
Volume 5, Issue 4, December 2019, Pages: 73-82
Received: Dec. 9, 2019;
Accepted: Dec. 20, 2019;
Published: Dec. 31, 2019
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Hashnayne Ahmed, Department of Mathematics, Faculty of Science and Engineering, University of Barishal, Barishal, Bangladesh
Shek Ahmed, Department of Mathematics, Faculty of Science and Engineering, University of Barishal, Barishal, Bangladesh
The exception of considering uncertainty could be very detrimental to the outcomes of any systems or phenomena in the long run. Stochastic Process describes the way of considering uncertainty in different sectors of our life. We use Linear Programming for planning at its best. It is also considered as the best optimization technique for taking decisions or planning. But this planning tool disappoints us in optimization for unexpected risk or stochasticity. Consideration of stochasticity for a farmer to devote land on different crops for harvesting could be some insurance for the farmer with the best possible outcomes. Stochastic Programming studies these types of optimization techniques with risk consideration for better decisions in every step of our life. In this paper, we described the early starting of uncertainty calculation or stochastic approach and the evolution of stochastic optimization fields. Stochastic optimization is rather important in the sense of uncertainty calculation than sensitivity analysis and works through data gained from experience. We also present a stochastic model with some uncertainty issues in harvesting to make better outcomes. Some application areas are also discussed.
A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis, International Journal on Data Science and Technology.
Vol. 5, No. 4,
2019, pp. 73-82.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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