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.
Thomas Eppel Continuing Lecturer Research Interests: Decision Analysis, Behavioral Economics
Zuguang Gao Assistant Professor Research Interests: Sustainable operations, Electricity market and energy systems, Control, optimization, and game theory, Reinforcement learning, algorithms design and analysis
Luyi Gui Associate Professor Research Interests: Sustainable Operations, Environmental and Public Policy, Operations in Developing Economies, Supply Chain Management, Optimization and Algorithm Design, Network Economics, Game Theory, Mechanism Design
Robin Keller Professor Emerita Research Interests: Creative Problem Structuring, Cross-Cultural Decision Making, Fairness in Decision Making, Decision Analysis Theory and Applications, Medical Decision Making, Multiple Attribute Decision Making, Probability Judgments, Ambiguity of Probabilities or Outcomes, Risk Analysis for Terrorism, Environmental, Health, and Safety Risks, Time Preferences and Discounting, Utility Models, Models of Risk
Ken Murphy Assistant Professor of Teaching
Carlton Scott Professor Emeritus Research Interests: Decision Making Problems, Optimization
Rick So Professor Emeritus Research Interests: Supply Chain Management, Production and Inventory Management, Manufacturing and Service System Design, Time-Based Management, Operations Research
John Turner Associate Professor Research Interests: Media planning / advertising allocation, Applied & large-scale optimization, Revenue management, Health care management
Shuya Yin Professor Research Interests: Supply chain management, Operations management, Cooperative and non-cooperative game theory in supply chains, Interface of operations management and marketing
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.
201B. Management Science
Organizations routinely seek to minimize costs or maximize profits, through the efficient use of resources and by effective planning and execution. Regardless of how much data a firm has, and how accurate its forecasts are, a firm nevertheless needs methods to transform predictions about the future into actionable plans and decisions. This course introduces students to prescriptive analytics and its application to management problems. The main topics are the management science core methodologies of optimization (problem formulation, solution methods, and sensitivity analysis), and simulation. We will apply these core methodologies to several functional areas of business, including operations, marketing, and finance. Effective use of management science techniques frequently saves businesses millions of dollars. The practice of management science relies heavily on computers, which use sophisticated algorithms to find optimal or near-optimal solutions to management problems. This course blends application with just the right amount of theory so that students always have a conceptual understanding of how to make good modeling decisions and to choose the right algorithm for the task at hand. We aim for students to become advanced users of optimization and simulation software, or managers with a keen eye for detail and an ability to manage technical staff implementing a management science project.
Effective use of management science techniques frequently saves businesses millions of dollars. The practice of management science relies heavily on computers, which use sophisticated algorithms to find optimal or near-optimal solutions to management problems. This course blends application with just the right amount of theory so that students always have a conceptual understanding of how to make good modeling decisions and to choose the right algorithm for the task at hand. We aim for students to become advanced users of optimization and simulation software, or managers with a keen eye for detail and an ability to manage technical staff implementing a management science project.
281. Analytical Decision-Making Models in a Digital World This course will introduce you to prescriptive analytics and its application to management problems. Prescriptive analytics is a collection of skillsets and methodologies which are strategically important to businesses in today’s digital world. Students will learn how to identify the key aspects of real-world logistical problems, build models to quantify the effects of anticipated outcomes and suggested courses of action, use software to find optimal or near-optimal solutions, and simulate the performance of suggested policies to estimate how our decisions may unfold in the real world. Examples include optimizing the advertising mix, multi-period inventory & production planning, portfolio optimization, online advertising, and trauma care system design. The course empowers students to apply prescriptive analytics to several functional areas of business, including operations, marketing, and finance.
282. Revenue Management Revenue Management studies how a firm should set and update pricing and product availability decisions across its selling channels to maximize profitability. It is the science of selling the right product to the right customer at the right time for the right price, and can be viewed as the demand-side complement to traditional supply-side inventory management. Enabled by digital technologies, revenue management is now pervasive across a broad range of industries. Using mathematical models and advanced analytics, students will study how airlines decide how many seats to reserve for high-paying business customers, how hotels determine when to discount their rooms, and how rental car companies determine how many reservations to overbook. Additionally, students will learn how auctions are used to price and sell online advertising, and discuss how revenue management is being used by the health care, retail, and entertainment industries.
283. Decision Analysis Facing many important and far-reaching decision situations in your professional and personal life, this class will provide you with the digital technology tools and thought processes to approach such situations with clarity and confidence and improve your decision making skills. This course will teach the use of decision analysis digital technologies for multiple objective decisions under certainty, decision-making under risk using decision trees, fitting probability distributions to judgments or data, and Monte Carlo simulation, applied to business, government, not-for-profit, and personal decisions.
285. Supply Chain Management This course introduces students to the tools and strategies to successfully manage uncertainty, meeting customer needs in the most timely and cost effective manner, and driving business disruptions through supply chain innovations. The use of advanced analytics and data-driven methods will be emphasized. Based on case studies, simulations, group discussions and guest lectures from practitioners, the course prepares students for managing supply chain challenges in practice such as the digital transformation, complex organizational network, globalization, and environmental and social responsibility concerns.
287. Project Management In this digital era characterized by the storms of technology changes, software upgrades, and communication system alterations, managers need to learn to manage the non-routine tasks related to and resulting from such rapid changes. Additionally, as companies constantly devise new products and services to stay competitive, the resultant tasks do not fit into the mold of business-as-usual. Organizing such tasks into projects affords managers with the ability to meet timelines, budget, performance goals, and expectations of many dissimilar stakeholders. This course equips students with tools and techniques to effectively manage projects in a rapidly changing environment. Using a project management framework and a computer software package, students will learn about the issues, problems, and solutions to carry out a team project from initiation to termination.
288. Predictive Analytics This course deals with predicting entities such as the demand for a product or service (commonly called forecasting) and predicting membership of known groups (commonly called classification). As such it is a blending of methodologies of forecasting and data mining. In particular we focus on multiple regression, logistic regression, neural nets, ARIMA, discriminate analysis and k-nearest neighbors. Although very technical and mathematical concepts lie behind these methodologies, our focus is more on the application of these methods to managerial problems and decision making.
In many examples, we will work with large data sets which will be split into training and validation sets in order to develop usable models. This approach facilitates model comparison with cross validation.
290. Fundamentals of Business Analytics With data fueling the digital transformation of enterprises, Fundamentals of Business Analytics will teach concepts on how to recognize and use meaningful data. This course will focus on the business understanding, the process of business analytics, and teaching a framework to understand what information forms the key drivers that could be fed into a mathematical model. Moreover, the course emphasizes how to make use of this information to drive digital change within organizations through analytics models that propose data-driven decision-making. In addition, this course will leverage case studies involving the digital transformation in automotive, retail, healthcare, entertainment, and other select industries to showcase how the analytics framework can be used to create new markets as well as products and services.
290. Redefining Operations in the Digital World This course will develop a process excellence driven approach for digital operations. While conventional Operations management processes leverage lean six sigma approach to excellence based on manufacturing practices, similar measures of excellence for digital operations will be necessary to minimize “defects” so that digital productivity could be defined, measured, analyzed, improved, and controlled.
Examples of digital operations in the industry where these processes are studied are in the internet and app driven consumer world, Robotic Process Automation (RPA), as well as digital manufacturing. Consumers are very demanding, and competition is fierce. To succeed in the “on-demand economy” a company needs to stand out from the crowd. Companies like Google, Amazon, Facebook, Netflix and Airbnb have developed ways of working that allow them to respond faster to consumer demands than their rivals (and are reaping the rewards). They have proven that even the largest companies can be as fleet-footed as a start-up. Case studies with these companies would be studied in this course.
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-OD2Research 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-OD5Game 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-OD6Large 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.