PhD Operations, Information, and Technology Courses
OIT 601. Fundamentals of OIT
The goal of this course is to provide first-year Ph.D. students in OIT with sufficient fundamentals to subsequently take advanced research seminars. The course covers the very basics of six topics: queueing theory, inventory theory, multi-echelon inventory theory, game theory, stochastic dynamic programming and econometrics. Lectures will be given by advanced Ph.D. students in OIT.
OIT 602. Dynamic Pricing and Revenue Management I
In tandem with OIT 603, this course explores the application of stochastic modeling and optimization to two closely related problem areas: (a) dynamic price selection, and (b) dynamic allocation of limited capacity to competing demands. As background, students are assumed to know stochastic process theory at the level of Statistics 217-218, microeconomics at the level of Economics 202N, and optimization theory at the level of MS&E 211, and to have some familiarity with the basic ideas of dynamic programming. Additional dynamic programming theory will be developed as needed for the applications covered. Emphasis will be on current research topics, especially in the realm of airline revenue management.
OIT 603. Dynamic Pricing and Revenue Management II
In tandem with OIT 602, this course explores the application of stochastic modeling and optimization to two closely related problem areas: (a) dynamic price selection, and (b) dynamic allocation of limited capacity to competing demands. As background, students are assumed to know stochastic process theory at the level of Statistics 217-218, microeconomics at the level of Economics 202N, and optimization theory at the level of MS&E 211, and to have some familiarity with the basic ideas of dynamic programming. Additional dynamic programming theory will be developed as needed for the applications covered. Emphasis will be on current research topics, especially involving customized pricing of financial services. OIT 602 is not a prerequisite for OIT 603 but is highly recommended.
OIT 655. Foundations of Supply Chain Management
This course provides an overview of research in supply chain management (SCM). It has three parts. The first part reviews basic tools of SCM research through selected readings in economics, IT and operations research. The second part reviews the literature in SCM, covering topics such as inventory models, information sharing, information distortion, contract design, value of integration, performance measurement, risk management, and the use of markets for procurement. The last part is devoted to recent advances in SCM research.
OIT 659. Operations Models in Homeland Security
In this course, we review the recent operations literature on homeland security. Topics include bioterrorism, pandemic influenza, nuclear security at ports and in cities, the biometric aspects of the US-VISIT program, the detention and removal of illegal aliens, suicide bombers, and electric power security.
OIT 660. Applied OIT
Description is currently unavailable because of ongoing review of the OIT PhD program by OIT faculty. Description will become available when the review is completed at the end of the Summer.
OIT 663. Methods of Operations/Information Systems
This course covers basic analytical tools and methods that can be used in research in operations and information systems. The emphasis is on foundations of stochastic inventory theory. Basic topics include convexity, duality, induced preference theory, and structured probability distributions. Much of the course is devoted to Markov decision processes, covering finite and infinite horizon models, proving the optimality of simple policies, bounds and computations, and myopic policies.
OIT 664. Stochastic Networks
Processing network models may be used to represent service delivery systems, multi-stage manufacturing processes, or data processing networks. The first half of this two-unit course consists of lectures on performance analysis (e.g., estimating congestion and delay) for classical product-form networks and for Brownian networks. The second half consists of student presentations of recent papers on managing processing networks, typically with game-theoretic aspects. Prerequisites: Statistics 217 and 218, or consent of instructor; some prior exposure to stochastic models in general, and queueing theory in particular, is useful but not essential.
OIT 665. Seminar on Information-Based Supply Chain Management
This seminar will highlight the research evolution and advances on the ssmart use of information in supply chain management. Such usage has helped companies sharing information to coordinate their supply chain and to realign their incentives. It has also helped reduce the so-called bullwhip effect. Latest information technology like RFID (radio-frequency identification) has also enabled visibility and structural changes that result in significant supply chain performance enhancements. This seminar will focus on the modeling approaches used by researchers that tried to capture the values and potentials of such applications.
OIT 667. Revenue Management
Systems for revenue management ? also called ?yield management? or ?revenue optimization? ? combine the use of information technology, statistical forecasting, and mathematical optimization to make tactical decisions about pricing and product availability. A familiar example is the passenger airline industry, where a carrier may sell seats on the same flight at many different fares, with fare availability changing as time advances and uncommitted capacity declines. This course will focus on the mathematical models that underlie contemporary revenue management practice, and on current research areas. The format will mix lecture and discussion with presentation of papers from the research literature. Prerequisite knowledge includes microeconomics, probability theory, optimization theory and dynamic programming, each at the level of an introductory graduate course.
OIT 669. Doctoral Management Science Seminar
This course introduces the students to research concepts, models and approaches in the area of Operations, Information and Technology (OIT). The course covers both modeling and empirical approaches, with papers from the entire OIT spectrum. The course will use a combination of (1) lectures, (2) a discussion of homework assigments, and (3) critical reading of research papers in the field.
OIT 670. Applied Dynamic Optimization
This course provides an introduction to the methods of dynamic programming, Markov decision processes, optimal control, and stochastic control, with particular emphasis on business applications. Application domains include manufacturing, supply chains, economics, and finance. While there are no prerequisites beyond basic probability theory and optimization, the course emphasizes topics that are not usually covered in a first course on dynamics programming or Markov decision processes, such as continuous time systems and general state spaces.
OIT 672. Stochastic Control in Operations and Economics
The first half of this course covers (a) the basic theory of Brownian motion, (b) Ito stochastic calculus, and (c) the rudiments of continuous-time stochastic control, all undertaken at a brisk pace, aimed at students who already know the basics or else have a strong enough math background to learn them quickly. The text for this part of the course will be Brownian Motion and Stochastic Flow Systems, by J. Michael Harrison, John Wiley and Sons, 1985. (The book is available as a scanned PDF file at http://faculty-gsb.stanford.edu/harrison/HarrisonBook.pdf.) The second half of the course will explore in depth two models arising in operations research and economic theory: (1) the Brownian version of the classic cash balance problem, or cash management problem, including both the version with only proportional transaction costs (treated in Chapter 6 of Harrison 1985), and the version with fixed plus proportional transaction costs (Harrison, Sellke and Taylor, Mathematics of Operations Research, 1983); and (2) Yuliy Sannikov's continuous-time principal-agent model (Review of Economic Studies, 2008). MS&E 322 (Stochastic Calculus and Control) provides ideal preparation, but this course is also suitable for students who have taken Statistics 310 A, B (measure theoretic probability) and have no previous exposure to stochastic calculus or stochastic control.