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.
| Published in | International Journal on Data Science and Technology (Volume 5, Issue 4) |
| DOI | 10.11648/j.ijdst.20190504.12 |
| Page(s) | 73-82 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Stochastic Optimization, Stochastic Programming, Stochastic Modeling, Uncertainty Calculation, LINDO
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APA Style
Hashnayne Ahmed, Shek Ahmed. (2019). A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis. International Journal on Data Science and Technology, 5(4), 73-82. https://doi.org/10.11648/j.ijdst.20190504.12
ACS Style
Hashnayne Ahmed; Shek Ahmed. A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis. Int. J. Data Sci. Technol. 2019, 5(4), 73-82. doi: 10.11648/j.ijdst.20190504.12
AMA Style
Hashnayne Ahmed, Shek Ahmed. A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis. Int J Data Sci Technol. 2019;5(4):73-82. doi: 10.11648/j.ijdst.20190504.12
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Download@article{10.11648/j.ijdst.20190504.12,
author = {Hashnayne Ahmed and Shek Ahmed},
title = {A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis},
journal = {International Journal on Data Science and Technology},
volume = {5},
number = {4},
pages = {73-82},
doi = {10.11648/j.ijdst.20190504.12},
url = {https://doi.org/10.11648/j.ijdst.20190504.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190504.12},
abstract = {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.},
year = {2019}
}
TY - JOUR T1 - A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis AU - Hashnayne Ahmed AU - Shek Ahmed Y1 - 2019/12/31 PY - 2019 N1 - https://doi.org/10.11648/j.ijdst.20190504.12 DO - 10.11648/j.ijdst.20190504.12 T2 - International Journal on Data Science and Technology JF - International Journal on Data Science and Technology JO - International Journal on Data Science and Technology SP - 73 EP - 82 PB - Science Publishing Group SN - 2472-2235 UR - https://doi.org/10.11648/j.ijdst.20190504.12 AB - 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. VL - 5 IS - 4 ER -
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