Optimization Models for Analytics

Catalog description

This course introduces students to fundamental quantitative methods for modeling, analyzing, and determining the best course of action in complex decision-making situations (i.e., prescriptive analytics). Topics to be covered include decision trees and tables, price of uncertainty, utility theory, linear programming (LP), LP sensitivity analysis, network optimization, integer optimization, and nonlinear optimization. Application areas include Finance, Marketing, Operations Management, and others (e.g., advertising, production and inventory planning, project or personnel scheduling, shipping and distribution, routing, ride matching, etc.).