Operations and Decision Technologies
The Operations and Decision Technologies program is designed to provide students with the foundations of management science and operations management. Examples of the disciplines studied include supply chain management, mathematical programming, decision analysis, network design and analysis, stochastic processes, queuing systems, inventory control systems, automated manufacturing systems, design and control of production systems and quality management.
MBA Core Classes
Statistics for Management
Decision Analysis [ITM Course]
Supply Chain Management
Service Operations [ITM Course]
Project Management [ITM Course]
Advanced Topics in Operations Management
Field Studies in Operations Management
Advanced Operations Management
Process Management and Modeling
Analytical Decision-making Models for Management
Course descriptions for the PhD program may be found on the PhD website, and the Undergraduate business classes may be found through UCI’s course catalogue.
PhD, Georgia Institute of Technology
Key Areas - Applications of optimization and game theory to collaboration and coordination activities in service operations and supply chains; sustainable/environmental operations, product take-back policies and economics; network economics, resource pricing and allocation
PhD, University of California, Los Angeles
Key Areas - 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
PhD, The University of New South Wales, Australia
Key Areas - Application of mathematical models in managerial decision making; development and analysis of optimization models arising from decision situations in business and industry
PhD, Stanford University
Key Areas - Supply chain management; production and inventory management; manufacturing and service system design; time-based management; operations research
PhD, Carnegie Mellon University
Key Areas - Media planning / advertising allocation; applied optimization; heuristics; revenue management
PhD, University of British Columbia
Key Areas - Supply chain management; operations management; cooperative and non-cooperative game theory in supply chains; interface of operations management and marketing
For the latest public research in Operations and Decision Technologies, click here.
Visiting & Affiliated Faculty and Researchers
201A Statistics for Management
Methods of statistical inference emphasizing applications to administrative and management decision problems. Topics include classical estimation and hypotheses testing, regression, correlation, analysis of variance, nonparametric methods and statistical probability.
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. The course uses spreadsheets extensively, including Excel built-in and add-in packages. Prerequisite: Basic course in calculus and algebra recommended; 201A recommended.
208 Operations Management
Introduction to strategic and tactical issues in production and operations management. A blend of quantitative and qualitative considerations. Topics: product planning, process design, capacity management, production planning, inventory control, distribution management, just-in-time manufacturing, quality management.
Forecasting is in the main process of organizing information about a phenomenon’s past in order to predict a future. As such, forecasts are critical inputs into the wide range of business decision making. Users include accountants, financial experts, human resource managers, production managers, and marketing people. Forecasting is a blend of science and art. In this course, we focus on the science associated with forecasting by studying various methodologies that are increasingly being used to support business decision making. In order to facilitate the use of these methodologies that often require very extensive computation, a computer oriented approach will be followed. Work of decision analysis is very general, you can use it for both professional and personal decision situations.
283 Decision Analysis
Should we launch an aggressive marketing campaign that will require substantial resources with no guarantee of success? Which job candidate should I hire? Which business model is most suited to support the long-term survival of my company? What information technology will best serve the needs of our customer service department? You will be facing many important and far-reaching decision situations in your professional life. Situations where substantial resources need to be committed, where many different stakeholder groups are involved in or affected by the decisions that you make, and where a variety of potential consequences are at stake. To make good decisions fast is becoming ever more important in a world where information is ubiquitous and technologies change at an incredible pace. This class will provide you with the conceptual framework and information technology tools to approach these situations with clarity and confidence and improve your decision making skills. Decision Analysis provides a systematic way to approach decision situations. It analyzes complex decision problems by breaking them into manageable pieces and by providing important insights that will lead to clarity of thought and commitment to action. Yet, Decision Analysis is not a "hard-nosed" calculation of the costs and benefits of various alternatives. Instead, it includes and encourages the exploration and representation of very personal and subjective values and opinions. Thus, it will put you and not some in control of your decisions. And, since the framework of decision analysis is very general, you can use it for both professional and personal decision situations. As an ITM elective, this class will provide you with the opportunity to immediately implement the conceptual and analytical tools using cutting-edge software. One of the textbooks comes with an academic version of one of the most widely used, industrial-strength software environments. We will also focus the content of exercises and cases on situations where advances in information technology have led to fundamental changes and new opportunities in business. In addition, we will hear from guest speakers who are actively involved in applying decision-analytic ideas and tools in the IT business environment.
285 Supply Chain Management
Matching supply with demand is a primary challenge for a firm: excess supply is too costly, inadequate supply irritates customers. Matching supply to demand is easiest when a firm has a flexible supply process, but flexibility is generally expensive. In this course, we will discuss how to assess the appropriate level of supply flexibility for a Global organization and explore strategies for economically increasing a company’s supply flexibility based on worldwide supply strategy, including outsourced manufacturing, supplies, resources, and the vast mesh of complex distribution network that all of the above are spread around. We will study coordination and incentives across multiple groups or players in a supply chain. While tactical models and decisions are part of this course, the emphasis is on the qualitative insights needed by general managers or management consultants. We will demonstrate that companies can use (and have used) the principles from this course to significantly enhance their competitiveness. The course applies best industry practices as well as academic research on Global Supply Chains to current industry problems, and allows students to test their learning through some simulation exercises where all the management techniques, tools, methodologies, and core insights are applied.
286 Analytical Decision-making Models for Management
This course introduces a number of quantitative models that are commonly used in decision support systems for managerial decision making. The emphasis of this course is on the applications of these quantitative models to business problems arising in diverse industries and functional areas including operations, finance, and marketing. The course is designed to develop your skills in formulating complex business problems as quantitative decision models and solving these models using Excel spreadsheets. Solutions to these decision models would provide useful managerial insights for the underlying business problems. Topics in this course include linear programming, integer programming, and computer simulation. Linear and integer programming models are widely used to analyze various business problems including product line planning, distribution networks, staff scheduling, and financial portfolios. Simulation models are commonly used to mimic the behavior of a system with uncertainty, and students will learn to build simulation models using the Excel add-in tool, Crystal Ball.
287 Project Management [ITM Course]
This course will cover both the qualitative and quantitative aspects of project management. Topics include how to evaluate and select projects, how to staff project organizations, planning, budgeting, scheduling, and resource allocations, and how to terminate and evaluate projects. The course will use a PC-based project manager for reporting and will emphasize management applications.
288 Advanced Topics in Operations Management
Delves more deeply into topics that are currently influencing advances in practice of operations management in both manufacturing and services industries. Topics include modeling and analysis of manufacturing systems, yield management, and workforce scheduling. Appropriate applications in Southern California included. Prerequisite: consent of instructor.
289 Field Studies in Operations Management
Participation in a small group project sponsored by local companies in Southern California. Involves the applications of various concepts taught in operations management and related areas to address real issues faced by the sponsoring companies. NOTE: Enrollment must be approved by instructor (resumes/projects will be matched).
290 Advanced Operations Management
This course builds on an introductory course in Operations Research/Operations Management by delving more deeply into issues of major current interest. The emphasis will be on contemporary practice in manufacturing and service industry and the new approaches that are revolutionizing the management of operations.
290 Forecasting and Data Mining
290 Process Management and Modeling
This course emphasizes the importance of effective operational planning in business process management. The course also covers the methods by which firms can improve their operational performance through effective business processes design. Students will use process management software to model business processes to understand the effect of various process designs.
290 Quality Management
Successful quality management touches every aspect of the corporation from the mission statement to each employee. The course begins with an introduction to quality as it pertains to the corporation and the approach of modern quality management to its implementation in the corporation. Student project teams study quality as it is implemented in different parts of the organization. The Six Sigma approach to achieving quality in the design, management and control of the production/service processes is studied. Readings, cases and lectures are used.
290 Revenue Management
Revenue Management focuses on 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. Using mathematical models and advanced analytics, we will study how airlines decide how many seats to reserve for high-paying business customers versus low-paying leisure customers, how hotels determine when to discount their rooms, and how rental car companies determine how many reservations to overbook. As well, we will study how auctions are used to price and sell online advertising, how advertising schedules are determined for several media vehicles, and how revenue management is being used by the health care, retail, and entertainment industries. We will solve the optimization problems which yield solutions to revenue management problems using Excel and Excel Solver, and discuss various modeling pitfalls and practical data issues. In addition, we will learn high-level concepts that general managers and management consultants can use to apply revenue management techniques across a broad spectrum of industries.
291 Decision Theory Doc Sem
A special topics course.
291 Large Scale Optimization
291 Game Theory and Its Applications in Supply Chain Management
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 Optimization Modeling and Methodology Part I
Part I - Nonlinear Programming: An overview of the different classes of nonlinear optimization problems with applications to management. Includes convexity and duality. (2 units)
291 Optimization Modeling and Methodology Part II
Part II - Integer and Network Programming: Types of network optimization problem. Binary integer and mixed integer programs. Application to management.
291 Ph.D. Sem-ODT
A special topics course.
291 Research Seminars in Supply Chain Management
This 2-unit 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 Stochastic Models in Operations and Decisions
This 2-unit doctoral seminar covers some fundamental concepts in queuing systems and dynamic programming. We also apply these models to analyze the optimal decisions in a number of stochastic operations.
295D Operations Lab
This course is designed to complement the concepts covered in the core Operations Management class on process modeling and analysis. Students in this lab will learn the basic skills of modeling and analyzing business processes using a commercial process modeling software. Through the applications of business cases and term project, the students will gain hands-on experience in building computer simulation models for decision making and improving the performance of the underlying business processes.
297T Decision Theory
(2-4 units) Decision theories and preference models: How models are elicited or theories are experimentally tested, relevance to different management research areas, alternative theories, applications in management practice, and interpretations for the general public. This course is available as a school-wide doctoral program course satisfying the theoretical breadth requirement. The course will be conducted in the seminar style, with individual students leading discussions on research papers and on their own research. Depending on students’ interests, topics may include: overview of decision analysis research and practice, with a focus on multiple attribute models, models and experiments on decision making with outcomes over time (discounting models and alternatives), modeling of real options in decision making, methods and experiments on valuing environmental outcomes, individual versus group decision making, especially with respect to ambiguity of probabilities, methods for structuring decision problems and eliciting utility functions and probabilities, Chinese vs. North American fairness judgments and choices, effects of having multiple choices on workers across cultures and professions, visual perceptions and decisions, such as effect of website ad layout on choices, multiple stakeholder decision processes, behavioral economics, behavioral finance, neuroscience and decision theory, medical decision making, effects of organizational goal setting on decision behavior, operations management topics and decision theory (i.e., loss aversion in supplier selection), portfolio decisions (i.e., capital asset allocation with multiple objectives for hospitals or homeland security), and happiness models and descriptive data.