Decision Under Risk

Catalog description

Analytics is “the scientific process of transforming data into insight for making better decisions”. For example, sales data can help us understand consumer purchase behaviors as well as demand patterns. These insights can be used to make sales forecasts, which in turn can inform assortment and production planning decisions. Optimization models have played a very important role in turning “insights” into “decisions” for companies in various industries: online advertising, airlines, energy, investment and finance, marketing, manufacturing, retailing, hospitality, etc. This course is aimed at enriching the exposure to business analytics techniques. Students will learn how to build simulation and optimization models that incorporate random parameters (e.g., demand, stock prices, market responses, etc.). It covers four chapters. The first one is on decision trees, a simple but powerful tool that explicitly allows to make optimal decisions in uncertain environments. We will use TreePlan, an Excel add-in to model and solve this type of problems. The second chapter is on advanced linear programming (LP), a follow-up from the contents you learned in Decision Models (DM). This part spans two topics: i) sensitivity analysis, which relates to understanding the impact of changing the parameters of a model on the optimal solution, and is executed using Excel Solver, and ii) solving LP models in python, where we will learn how to use the Pyomo modeling language. The “pythonic” component of this part builds upon the python intro you learned in the Dealing with Data course. The third chapter builds on the simulation topic covered in the DM course. We will discuss how to interpret the output of simulation models in terms of risk evaluation. We will be using Crystal Ball, as it was done in DM, and we will learn how to implement this type of models in Python. We will discuss how to use…