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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.

Queueing models may be used to represent service delivery systems, 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) and control of queueing systems using asymptotic methods, both in the traditional heavy traffic regime and in the Halfin-Whitt regime. The second half consists of student presentations of recent papers in asymptotic methods in queueing systems. 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.

This seminar will highlight the research evolution and advances on the smart use of information in supply chain management. Advances in technologies like real-time information systems, decision support methodologies, the internet and mobile technologies such as RFID (radio-frequency identification) have also enabled visibility and structural changes that result in significant supply chain performance enhancements. In parallel to the development of new practices and concepts in industry, we will examine emerging research that are based on (1) structuring new processes and supply chain networks with the new technologies; (2) exploring ways to do planning and make decisions consequently; (3) quantifying the benefits as a result; and (4) aligning the incentives of multiple players in a supply chain when the costs and benefits to these players are different.

The goal of this course is to provide a comprehensive introduction to the theory and practice of revenue management. It will comprise of a set of lectures that will cover the theoretical fundamentals of the area as well as an overview of current research developments through the presentation and discussion of recent papers. Topics include capacity control (single-resource and network), consumer behavior and market response models, dynamic pricing, procurement auctions, price experimentation, supply chain management and pricing.

The first half of this course will cover (i) the basic theory of Brownian motion, (ii) Ito stochastic calculus, and (iii) 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 Models of Performance and Control, by J. Michael Harrison, Cambridge University Press, 2013, which can be ordered from Amazon: http://www.amazon.com/Brownian-Performance-Control-Michael-Harrison/dp/1107018390/ref=sr_1_1?ie=UTF8&qid=1395420072&sr=8-1&keywords=Brownian+Models+of+Performance+and+ControlnnnThe second half of the course will explore in depth some models arising in operations research, finance and economic theory, such as the McDonald-Siegel investment model (an optimal stopping problem, treated in Chapter 5 of the textbook), Brownian versions of the classic cash balance problem (a family of stochastic control problems, treated in Chapter 7 of the textbook), and Yuliy Sannikov’s continuous-time principal-agent model (Review of Economic Studies, 2008). The course will be rather informally organized, more of a collaboration between students and instructor than a top-down lecture format, with at least half of the class time devoted to presentation of problems by students and auditors.

This course aims to introduce students to research topics in data-driven decision making with specific attention to healthcare applications. However, most concepts are applicable in areas beyond healthcare as well. Examples of topics are: prediction and risk adjustment, computational and statistical challenges associated with large-scale data, and dynamic decision making under uncertainty.

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.