15.053 Optimization Methods in Business Analytics Instructor:   James B. Orlin

 Menu This website is for MIT students who want to learn more about 15.053, Optimization Methods in Business Analytics.  Welcome! 15.053 is an introduction to optimization models and methods.  1.     REST 2.     Core subject in  15-2 major and minor in business analytics. 3.     Elective in the minor in Statistics and Data Science. 4.     Satisfies optimization requirement in  6-14 major: Computer Science, Economics and Data Science.   Instructor:   Jim Orlin James Orlin is the E. Pennell Brooks (1917) Professor in Management and a Professor of Operations Research at the MIT Sloan School of Management.   Course content and goals In 15.053, we present modeling techniques in optimization that are known as linear programming, integer programming, and nonlinear programming.  Students can model optimization problems using spreadsheet optimization -- e.g., Excel and Excel Solver (tutorial, spreadsheet) -- or using an algebraic modeling language (Julia and JuMP).  We also describe algorithms for optimization problems as well as general purpose heuristics.  The Spring 2019 syllabus of 15.053 is here.  The subject evaluations for 2017 to 2019 are here.   Here are (selected) student comments from past years.  Our goal in 15.053 is to help students develop an "optimization mindset".  We want students to look out at the world and see optimizations problems everywhere, and to recognize when these problems can be modeled, analyzed, and solved.

 ÒIÕve enjoyed my time at MIT and Sloan tremendously. My favorite business course has been Optimization Methods with Professor Orlin. Together, my friends and I were able to do a project where we used Python code to build a simulation of a Red Sox lineup. Adopting an optimization mindset and seeing how businesses find the most efficient ways to do things made this course one of the most applicable for me. Austin Filiere.   MIT '18.   Drafted by Chicago Cubs in 2017.   (From an article in Poets and Quants.)

Applications and the course project.

 Optimization models and methods can be applied to management, engineering, and science, and more.  Within 15.053, we show how to optimize problems within machine learning and statistics, sports analytics, finance, operations, marketing, as well as other domains. Students have an opportunity to apply what they learn in 15.053 in their course project.  Students often choose projects of importance to MIT or of personal interest to themselves.

Among the past 15.053 projects were:

á                 optimizing hours for dining facilities

á                 optimal allocation of freshmen to dorms

á                 optimal allocation of gates at Logan airport

 INFORMS, the professional society for Operations Research and Business Analytics, defines Analytics as "the scientific process of transforming data into insights for making better decisions." You can learn more about operations research and analytics at the "Student Union" website sponsored by INFORMS.

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