Course Schedule and Catalog

Department: Industrial Engineering

Meeting: 4:00PM - 6:29PM T (12-JAN-2026 - 24-APR-2026) 

Part of Term: Full Term

Grade Mode: Standard Letter

Course Type: Lecture

Language of Instruction: Taught in English

Method of Instruction: Face to Face

Credit Hours: 3

 

Section Max Enrollment: 30

Section Enrolled: 4

Total Cross-list Max Enrollment: 30

Total Cross-list Enrolled: 20

Enrollment data as of: 25-NOV-2025 11:32AM

 

 

Final Exam: GR Course-Dept Schedules Exam

 

Description: Ever wondered why airfares change? Why a person in the seat next to yours got a better deal or perhaps why you were able to fly to your desired destination for pennies? And, why airlines often ask whether you are willing to take another flight in exchange for a voucher? The above are just one sort of examples where prices for travel services seem to change for reasons that are not immediately clear. People have been intrigued about these practices and with the advent of the already ubiquitous e-commerce prices that change dynamically are part of everyday life. Revenue management ('RM') and dynamic pricing are the science engines that power that dynamic. In this course we will study both theory and practice of revenue management and dynamic pricing with a focus on its application areas. The goal is to find a scientifically sound answer to the question of what product a firm should offer to a customer at any given time, given capacity constraints and, possibly, some other information. This economics problem has often been paraphrased as ‘selling the right product to the right customer at the right price’ and has become acute in thin-margin industries such as airlines (after deregulation in the 70’s), where even a small improvement in revenue implies a major improvement to profit. Several early adopters in other industries having similar features followed shortly, e.g., hotels, cruise lines, and car rental companies. The wealth of information and the ease of modifying customer offering that came with the advent of the Internet spurred an incredible expansion in the number of industries adopting the discipline of RM and gave rise to several closely related areas of new techniques and applications such as dynamic pricing, offer optimization, multi-sided markets (e.g. ride-sharing platforms, online auctions) and other aspects of e-commerce. In the course we will start with the basic RM concepts and an overview of the most popular fields of applications. Then we will explore in depth the science behind most popular techniques used there. Therefore, at least some level of quantitative background will be required (OR, STEM). We will cover main features of state-of-the-art revenue optimization algorithms, as well as forecasting and estimation of demand models that are applicable to real-life data. Forecasting and optimization are two crucial buildings blocks in typical RM or pricing applications, and we will show how these can be enriched with more recent data-driven and machine learning techniques. Cross-list: CMOR 465. Recommended Prerequisite(s): INDE 571 & INDE 545 Mutually Exclusive: Cannot register for INDE 565 if student has credit for CMOR 465.