15.053  
Optimization methods in business analytics

 Course Content and Goals

Optimization is an important subfield of operations research and business analytics (see below).  The field of optimization is often referred to as "prescriptive analytics."   Its purpose is to determine the best possible solutions for an organization.  This contrasts with "predictive analytics" whose purpose is to identify the likelihood of future outcomes based on historical data.

Optimization models have been of great value within business, engineering, as well as science. In 15.053, students will see applications of optimization modeling in logistics, manufacturing, statistics, machine learning, transportation, game theory, marketing, project management, and finance.  Optimization models also form the basis of neural net technologies as applied to machine learning.

In 15.053, students learn how to express optimization models conceptually (on paper) and then translate these models to a computer using either spreadsheet software (such as Excel) or an algebraic modeling language (such as JuMP, which is written in the Julia language.)  After translating the model so that it can be understood by a computer, students can use state-of-the-art solvers such as Gurobi to obtain an optimal solution.    

Students learn several types of optimization modeling, including linear programming, network optimization, integer programming, nonlinear programming, and dynamic programming.  Students also learn basic solution methodologies for these optimization models.

There are two group projects for 15.053.  These projects vary from year to year. 

Our goal in 15.053 is to help students develop an "optimization mindset".  We want students to look out at the world and see optimization problems everywhere, and to recognize when these problems can be modeled, analyzed, and solved.