Carnegie Mellon University

Decision Analytics for Business and Policy

Course Number: 12-768

This course introduces modeling frameworks and computational tools to address complex, ill-defined, large-scale decision-making problems that arise in policy and business.

Using a combination of lecturing, case studies and class discussions, it covers advanced methods of decision-making under uncertainty in four major areas: large-scale optimization, discrete event simulation, stochastic optimization and queuing theory.

It will focus on modeling (how to formulate models to address policy- or business-relevant problems), computation (how to solve large-scale problems) and applications in policy and business (how to integrate viewpoints of different stakeholders, how to select the scope of the model, etc.).

Applications are drawn from a variety of real-world settings in transportation, energy, information systems, health care, supply chain management, etc.

Participants are expected to take active learning roles in the computational application of the materials presented in class using the R programming language and the CPLEX optimization solver.

A term project simulates realistic and challenging issues where new solutions need to be developed, implemented and communicated.

Semester(s): Spring
Units: 12
Prerequisite(s): An introductory course in Operations Research, such as Management Science I and II or Decision-Making under Uncertainty