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OIT 245: Optimization and Simulation Modeling

This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, supply chain management, revenue and yield optimization.
Units: 3 | Grading: GSB Letter Graded

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.
Units: 3 | Grading: GSB Letter Graded

OIT 248: Optimization And Simulation Modeling - Advanced

This course constitutes an advanced option in the menu of classes satisfying the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is a superset of OIT 245 and OIT 247, starting with a very fast paced overview of basic concepts, and quickly diving into more advanced topics and software tools. By the end of the course, students should (1) leave with a good understanding of different types of optimization and simulation models and when they are useful; (2) be able to solve real-world models using up-to-date software; (3) when faced with a business problem, be able to identify what type of optimization model is appropriate, and how to set it up most efficiently; (4) be able to understand and discuss model outputs in a critical fashion. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. We will be using Python as the basic software, complemented with suitable packages for formulating and solving optimization models (e.g., the Gurobi software) and for conducting Monte Carlo simulation. Students should be comfortable using these software packages by the end of the class, but no specific prior experience with these packages is necessary. Some prior coding experience is helpful, but is not a strict requirement for the course.
Units: 3 | Grading: GSB Letter Graded

OIT 536: Data for Action: From Insights to Applications

Data for Action is an MBA short course dedicated to identifying value in and creating value from data. It deals with the technical, legal, regulatory and business strategic decisions that must be considered when delivering solutions to customers.
Units: 2 | Grading: GSB Letter Graded

OIT 605: Behavioral Operations Management

Behavioral Operations incorporates insights from cognitive psychology, social psychology and behavioral economics to study how individuals make decisions in an operational context. Two major goals of Behavioral Operations are to provide a better understanding of (and make better predictions about) behavioral regularities in operational settings, and to provide guidance to firms on how to design mechanisms that will lead to better decisions and improved performance. This course has several aims: (1) To survey foundational research from economics and psychology on important behavioral factors such as bounded rationality and decision heuristics, folk intuitions about random processes, preference regularities such as loss aversion and reference dependent preferences, and interpersonal factors such as trust and fairness. (2) To apply behavioral insights to core operational settings such as inventory decision making, queueing systems, supply chain relationships, contracting, etc. (3) To discuss how to conduct behaviorally-inspired research using a range of methodologies including analytical modeling, laboratory and field experiments, and observational empirics. There will be a particular emphasis on laboratory experimental design, and in in many cases we will examine series of experiments to see how experiments can build on each other ¿ especially when researchers with different theoretical predispositions look at the same issue. Our goal is to help students identify behavioral issues to incorporate into their research interests, as well as opportunities to engage in experimental research as an extension of their current research agenda. This course meets the behavioral requirement for OIT PhD students.
Units: 2 | Grading: GSB Letter Graded

OIT 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, MKTG 691, OB 691, POLECON 691, STRAMGT 691)

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.
Units: 1-15 | Repeatable for credit | Grading: GSB Pass/Fail

OIT 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MGTECON 692, MKTG 692, OB 692, POLECON 692, STRAMGT 692)

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.
Units: 1-15 | Repeatable for credit | Grading: GSB Pass/Fail

OIT 698: Doctoral Practicum in Teaching

Doctoral Practicum in Teaching
Units: 1 | Repeatable for credit | Grading: GSB Letter Graded

OIT 802: TGR Dissertation (ACCT 802, FINANCE 802, GSBGEN 802, HRMGT 802, MGTECON 802, MKTG 802, OB 802, POLECON 802, STRAMGT 802)

Units: 0 | Repeatable for credit | Grading: GSB Pass/Fail
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