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This B case follows the A case “Roqueta Origen: Streamlining Wine Storage and Mixing (A)” (UVA-QA-0978). It resumes where the A case concluded and explores several different approaches for optimizing Roqueta Origen’s wine allocation. Each approach has limitations, prompting the consultant to consider alternate ways to solve the case problem. The case is published with supplemental data files featuring data and solutions for the different wine allocation approaches to support a quantitative exercise.
- Identify and evaluate the advantages and disadvantages of different optimization formulations for a complex real-world scenario without a clearly dominant solution. - Learn how to communicate with and manage analysts with optimization expertise. - Understand how to formulate nonlinear mixing problems and linearize nonlinear terms using binary variables. - Appreciate that sometimes a sequence of simple optimization problems can approximate and outperform a more complex model. - Understand how optimization techniques can be applied to improve operational efficiency in horticulture. - Interrogate solution output to evaluate model performance. - Learn how mathematical notation is used to communicate and formalize optimization formulations. - Appreciate what makes an optimization formulation easy or difficult to solve. - (Optional) Consider how we can use large language models (LLMs) to help us prototype and solve optimization problems. - (Optional) Learn how to use Gurobi and Python to solve large-scale optimization problems.