2622. A two-stage stochastic programming approach for optimizing wine purchasing decisions considering climatic uncertainty
Invited abstract in session MC-16: Optimization in Agriculture, stream Sustainable Food & Agroforestry.
Monday, 12:30-14:00Room: Esther Simpson 2.07
Authors (first author is the speaker)
| 1. | Carlos Monardes
|
| School of Engineering, Universidad Católica del Norte | |
| 2. | Alan Toledo
|
| School of Engineering, Universidad Católica del norte | |
| 3. | Elbio Avanzini
|
| Industrial and Systems Engineering, Pontificia Universidad Católica de Chile | |
| 4. | Mauricio Varas
|
| Universidad del Desarrollo | |
| 5. | Franco Basso
|
| Pontificia Universidad Católica de Valparaíso | |
| 6. | Raul Pezoa
|
| School of Industrial Engineering, Universidad Diego Portales |
Abstract
Wine supply chain planning faces several operational challenges, including uncertainty in grape production due to climatic variations. This paper proposes a novel two-stage stochastic programming formulation for supporting tactical decision-making processes in winemaking, including market policies, farms, and industrialization. Previous studies have focused mainly on isolated links in the supply chain without comprehensively considering the impact of uncertainty in multiple stages of the process. The methodology used relies on an optimization model structured in two steps. The first focuses on decision-making regarding acquiring resources and raw materials by buying grapes with forward contracts considering variations in grape quality due to climate. The second covers the operational management of the resources obtained, including allocating grapes to fermentation tanks and scheduling them. The solution obtained is verified to reach the established market quota. Otherwise, the model can react by buying spot contracts. Therefore, wine production is formulated according to the availability of bulk wine to satisfy the market. This approach provides excellent flexibility in uncertainty management, offering efficient solutions with few computational resources. The model's effectiveness is evaluated through simulations, providing valuable insights into the trade-offs between resource allocation, production costs, and the ability to meet demand under uncertainty.
Keywords
- Capacity Planning
- Stochastic Optimization
- OR in Agriculture
Status: accepted
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