Product Overview

This C case concludes the scenario introduced in the A case “Roqueta Origen: Streamlining Wine Storage and Mixing (A)” (UVA-QA-0978). The C case presents two final approaches to Roqueta Origen’s wine allocation dilemma. The final approaches reveal the solution to the optimization challenge and address the limitations of the approaches presented in the B case (UVA-QA-0979). The case is published with supplemental data files featuring data and solutions for the two final wine allocation approaches to support a quantitative exercise.

Learning Objectives

- 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.