MANAGING UNCERTAINTY IN AGRICULTURAL PRODUCTION: A TWO-STAGE STOCHASTIC PROGRAMMING APPROACH
Loading...
Authors
Cahir, Sean
Subjects
uncertainty
stochastic optimization
agriculture
two-stage stochastic optimization with simple recourse
optimization
loss-minimization
stochastic optimization
agriculture
two-stage stochastic optimization with simple recourse
optimization
loss-minimization
Advisors
Royset, Johannes O.
Date of Issue
2023-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Agriculture, a crucial contributor to Australia’s GDP, exports, and economy, involves inherent risk and uncertainty. Amidst these challenges, Australian farmers must craft optimal crop and livestock strategies to minimize risk whilst meeting their objectives. Traditional mathematical programming methods have aided resource allocation but fail to adequately address the uncertainty surrounding future market conditions and input parameters. This thesis explores two-stage stochastic optimization to enhance Australian small farm performance under uncertainty. We model uncertain events impacting farm operations as probability distributions, aiming for improved resource allocation and risk management. The stochastic program maximizes mean profit, worst-case profit, and optimizes the superquantile. Compared to deterministic approaches, our model increases the mean profit by 4.3%, raises the lowest 10% profits by 20.5% via the superquantile objective, and elevates the minimum profit by 140.8% when maximizing the worst-case profit. Our approach facilitates strategic planning and risk management within Australia's farming sector.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.