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2579. Valuing harvesting technologies rental cost using a simulation approach
Invited abstract in session MD-12: OR in Agriculture, stream OR in Agriculture and Forestry .
Monday, 14:30-16:00Room: 13 (building: 116)
Authors (first author is the speaker)
| 1. | Sergio Maturana
|
| Ingenieria Industrial y de Sistemas, P. Universidad Catolica de Chile | |
| 2. | Elbio Avanzini
|
| Industrial and Systems Engineering, Pontificia Universidad Católica de Chile | |
| 3. | Carlos Monardes
|
| School of Engineering, Universidad Católica del Norte | |
| 4. | Pablo Acevedo
|
| Pontificia Universidad Catolica de Chile |
Abstract
Which harvesting technology to use is a crucial decision for farmers, particularly when dealing with perishable products. Renting machinery is customary for many farmers, with costs fluctuating based on the machines’ features, yearly conditions, and the producer's willingness to pay. Negotiations on rental discounts often occur, especially when renewing machine rentals.
Employing simulation and decision rules, we assess the problem of choosing between different technologies that a wine grape farmer might use when harvesting. Besides the cost differences, the machines behave differently in rainy conditions. The occurrence of rain and its intensity is difficult to predict when the farmer must choose which machine to use. The farmer might also decide not to rent any equipment, so they won’t be able to harvest for some time.
The farmer navigates harvesting decisions through a multidimensional utility function applied across numerous scenarios spanning several years, with rainfall intensity as the critical uncertainty. We performed numerical experiments using Monte Carlo simulation to evaluate the use of different technologies. We analyzed the results to determine how well the proposed approach can consider the operational complexity of harvesting using different types of machines and under uncertain weather conditions. This can help make a better decision when choosing which machine to use in the initial year and the switching costs in subsequent years.
Keywords
- Decision Support Systems
- OR in Agriculture
- Simulation
Status: accepted
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