Menu

Operations and Decision Technologies

The Operations and Decision Technologies area studies how business processes function, and how to employ advanced analytics to make optimal or near-optimal decisions in a variety of business contexts. As our world becomes increasingly digital, the technologies that automate business processes and link them to human systems generate more data – bigger data – than ever before. We use this data to uncover the fundamental business relationships that drive operational excellence, build analytical models that communicate core system dynamics, illustrate consequences to actions that may not be immediately obvious, and develop computational methods that assist decision-makers by suggesting one or more near-optimal solutions.

Courses offered by the Operations and Decision Technologies area draw on cutting-edge theory and research. Methodological topics include optimization, simulation, statistics, predictive analytics, decision theory, collaborative and non-collaborative game theory, networks, queueing theory, stochastic modeling and dynamic control.  Application areas include supply chain management, logistics, inventory management, revenue management and pricing, sustainability and reverse supply chains.

Faculty

Luyi Gui
PhD, Georgia Institute of Technology

Robin Keller
PhD, University of California, Los Angeles

Carlton Scott
Professor Emeritus
PhD, The University of New South Wales, Australia

Rick So
PhD, Stanford University

John Turner
PhD, Carnegie Mellon University

Shuya Yin
PhD, University of British Columbia

Lecturers and Researchers

Coursework

Undergraduate business classes may be found through the UCI course catalogue.

MBA Core Course Descriptions

MGMT HC 201B. Operations Analytics for Healthcare Executives
This course introduces you to quantitative methods and their application to management problems. The practice of operations analytics relies heavily on digital technologies, using sophisticated algorithms to find optimal or near-optimal solutions to management problems, such as evaluating the cost-effectiveness of medical treatments, assigning hospital staff, scheduling operating rooms, routing equipment deliveries, locating trauma centers, and setting contract terms for revenue management in hospitals. Our focus will not be on understanding the inner workings of these algorithms, but rather on formulating problems so the computer knows how to solve them. 

The main topics include Monte Carlo simulation, optimization via linear and integer programming models, and decision analysis (multiple objective decisions under certainty and decision-making under risk using decision trees). We will apply these models and ways of thinking to several functional areas of business, as well as to decisions about health and personal lives, using software such as Excel for multiple attribute decisions under certainty and optimization and the TreeAge package for decision tree analysis and simulation for decisions under risk.

Operations analytics (also called management science) should be viewed as a toolkit that will empower you to enact meaningful improvements in the organizations that you will be part of. Effective use of the right tools frequently saves businesses millions of dollars, and this course will teach you how to properly use some of the most effective tools in the box. Moreover, you will learn the vocabulary of operations analytics and learn to think like a management scientist, so that you may direct strategic implementations of digital technology tools in the organizations you work with in the future.

MBA 208. Operations Management
Operations Management is concerned with analyzing business processes involved in production and delivery of goods/services to meet customers’ demands. We discuss the core concepts in the development and management of these processes and apply them to businesses that are operated in today’s digitally-driven business environment. For example, we study how digital technologies affect the applications of these core concepts, such as how Starbucks’ business process is influenced by mobile orders, how robots change the efficiency in e-commerce fulfillment, and how data analysis on a firm’s inventory shapes the firm’s future potential value. Through critical analysis of business processes, the students will gain a good understanding of a number of major issues in successfully managing both manufacturing and service operations in an inter-connected world.

This course provides a blend of qualitative and quantitative treatment for understanding process performance and operations issues. A combination of lectures, cases, videos and in-class mini cases/exercises will be used.

MBA Elective Descriptions

201B. Management Science
An introduction to computer-based models for decision making. Topics include optimization (linear programming, integer programming, network flow models) and computer simulation. Uses spreadsheets extensively, including Excel built-in and add-in packages.

280. Forecasting
Basic theory and techniques used to forecast future activities in technological, economic, social, and political arenas. Impact of forecasting on managerial decision making.

281. Analytical Decision Models
An introduction of analytics tools for decision making. Topics include linear and non-linear optimization and simulation models. Excel Solver will be used as the optimization tool and Risk Solver Platform will be used as the simulation tool.

282. Revenue Management
Students learn to apply advanced analytics to earn incremental revenue through the efficient use of resources and science-based pricing methods. Statistics and optimization (using Excel and Excel Solver). Industry-specific implementation issues.

283. Decision Analysis
Models of preferences and uncertainty; exercises in creative problem solving. Assessment and use of preference models (von Neumann-Morgenstern expected utility and measurable value functions) for private, public, and not-for-profit decision making. Assessment and use of subjective probabilities in decision making.

285. Supply Chain Management
Introduces students to the tools and strategies to effectively match supply and demand. Focuses on the coordination of material and information flows in supply chains. Recent innovations are also discussed, including globalization, the impact of electronic commerce, and sustainability issues.

287. Project Management
Examines the fundamental components of project management and its role in the modern corporation. Emphasis on how to initiate, implement, control, and terminate a project. Use of computer package for project management.

288. Predictive Analytics
Deals with predicting entities, such as demand for a product or service (forecasting) and predicting membership of known groups (classification). Blends methodologies of forecasting and data mining and focuses on the application of these methods to managerial problems and decision-making.

290. Special Topics in Operations & Decisions Technologies
Studies in selected areas of operations & decisions technologies. Topics addressed vary each quarter. Past courses include: Fundamentals of Business Analytics, Forecasting and Data Mining.

PhD Course Descriptions

291. PhD Sem-ODT
A special topics course.

291-OD1 Stochastic Models in Operations and Decisions  (2 units)
This doctoral seminar covers some fundamental concepts in queueing systems and dynamic programming. We also apply these models to analyze the optimal decisions in a number of stochastic operations.

291-OD2 Research Seminars in Supply Chain Management  (2 units)
This doctoral seminar provides some basic knowledge in several key research issues in supply chain management. We discuss a number of current research topics and challenges in supply chain management research. 

291-OD3 Optimization Modeling and Methodology   Part 1: Nonlinear Programming  (2 units)
An overview of the different classes of nonlinear optimization problems with applications to management. Includes convexity and duality.

291-OD4 Optimization Modeling and Methodology    Part 2: Integer and Network Programming  (2 units)
Types of network optimization problems. Binary integer and mixed integer programs. Application to management. 

291-OD5 Game Theory and Its Applications in Supply Chain Management  (4 units)
This Ph.D. seminar course introduces some fundamental concepts and methodologies in cooperative and non-cooperative game theory and their applications in supply chain models. Each class is a combination of lectures and class discussions. 

291-OD6 Large Scale Optimization  (4 units)
This doctoral course explores various computational techniques that are useful for solving optimization problems with a large number of variables and/or constraints.  We will study general techniques for computing optimal solutions to large problems using iterative methods, as well as ways to aggregate the solution space of some types of problems to yield near-optimal solutions. We will study Lagrangian relaxation, column generation, Dantzig-Wolfe decomposition, and Bender’s decomposition, from both a theoretical and practical perspective.  Students will learn to formulate and solve large-scale problems using the modeling language AMPL, and learn how to exploit these techniques for their own research. 

291-OD7 Network Models and Application (4 units, anticipated for 2014-2015, Gui)
The course introduces students to the optimization and game theoretic tools to study network systems. We also survey applications of network models in transportation, supply chain, Internet, e-commerce, and social networks. The course involves a mixture of lectures and discussion seminars.

291-OD8 Stochastic Programming (2 units, anticipated for 2014-2015 Lejeune)
This course will focus on Stochastic Modeling and Programming. Stochastic Programming is a discipline intersecting with probability theory and statistics on one hand and with mathematical programming on the other hand. It is a framework for modeling optimization problems that involve uncertainty. While deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown ones; their eventual outcome depends on the future realization of random events. Stochastic Programming relies upon the fact that probability distributions governing the data are known or can be estimated. The goal here is to find some policy that is feasible for (almost) all possible realizations and optimizes a function of the decision and the random variables. More generally, such models are formulated, analytically or numerically solved, and studied in order to provide useful information to the decision-maker.

291-OD9 Convex Math Programming: Optimization & Decomposition (4 units, Turner)
This doctoral course introduces students to the field of mathematical programming through the lens of convex optimization. We will study the theory of convex optimization, and learn how to identify, formulate, transform, and solve convex optimization problems.

Convex programs are an important class of mathematical programs because (1) many problems can be formulated as convex programs, and (2) we have efficient techniques to find globally optimal solutions to convex programs.  However, translating and formulating a given problem as a convex program is not always easy; in fact, it can require a high level of expertise to verify that a math program is indeed convex.  In this class, we will introduce a methodology called disciplined convex programming (DCP), which defines a set of rules derived from convex analysis  If a math program is formulated following the DCP rules, it is guaranteed to be convex, eliminating the need to verify its convexity post-construction.

We will also study how classical decomposition techniques (e.g., column generation, Dantzig-Wolfe decomposition, Benders decomposition, and Lagrangian relaxation) can be helpful when solving large-scale convex optimization problems.

Students will choose a project which can be modeled as a convex optimization problem, and put to practice what they have learned using the modeling languages AMPL, MATLAB, CVX, and CVXPY. The techniques we will cover are applicable to a wide variety of business and engineering applications, and students are encouraged to choose a course project that is in line with their current research interests.

There are no formal prerequisites for this course beyond having a level of mathematical maturity which is expected of a PhD student at the Paul Merage School of Business. For example, it is expected that you know matrix algebra and multivariable calculus.  Given that students may come from different backgrounds, I do not assume that students have a working knowledge of optimization theory. To get everyone up to speed, I will cover some background material in the first two lectures. But most importantly, if at some point during the course I start using terminology that you are unfamiliar with, please point this out so I can summarize any concepts which are unclear. 

291-O10 Nonlinear Optimization (2 units, Scott)
Modelling nonlinear optimization problems, properties, geometric programming, convex programming, signomial programming, nonconvex programming, entropy optimization, applications to operations management, transportation planning, location, statistics and data mining.