Below is a sample course path that you would take as a student while earning your doctorate in Decisions, Operations & Technology Management. In addition to the classes listed, all DOTM PhD students participate and enroll in the DOTM Seminar Series each quarter, which showcases student, faculty, and guest speaker presentations of ongoing research.
Below this sample course path, you will also find a listing of all of the Management courses we offer as part of our Ph.D. program. For course descriptions for courses outside the Management area, please see the UCLA General Catalog.
Our course offerings are just one facet of your time as a student in our program. To learn about the entire program, please click [here].
DOTM Doctorate Level Courses
203A: Economics of Decision
Requisite: basic probability theory. The basics of single-person decision theory and an introduction to non-cooperative game theory (during the last few weeks of the quarter). The von Neumann-Morgenstern expected utility theory is examined in some detail. Other topics in decision theory are subjective expected utility theory and departures from expected utility behavior.
210A: Mathematical Programming
Requisite: linear agebra. A comprehensive development of the theory and computational methods of linear programming, with applications to a variety of areas.
210B: Applied Stochastic Processes
Requisites: probability theory at the level of Math 170A or Statistics 100A or EE 131A. Topics include Poisson processes, renewal theory, Markov chains, and Markov decision processeswith an emphasis on problem formulation, decision making, and the characterization of optimal policies. Specific applications include traditional operations research topics (inventory, queuing, maintenance, reliability) as well as several in microeconomics (e.g., search and R&D).
210C: Network Flows and Integer Programming
Requisite: linear programming. Survey course with three objectives: (a) to lay foundations for more advanced study of graphs, network flow models, and integer programming models and their applications; (b) to establish connections between these technical foundations and real problems drawn from many areas of management; (c) to build some of the professional skills needed to apply these tools.
211A: Nonlinear Mathematical Programming
Requisites: 210A and Math 32A or equivalent. The theory, methods, and applications of optimization for situations where the models must be nonlinear, with special emphasis on the case of "convexity". Topics include classical approaches to optimization, the theory of optimality and duality, the main computational approaches, and a survey of currently available computer software.
211B: Large-Scale Mathematical Programming
Requisites: 210A or equivalent. The theory, methods, and applications of optimization for situations where the models are large and have special structure, as is often the case in real applications. The focus is on ways of exploiting special structures with combinatorial, multidivisional, and stochastic aspects in pursuit of computational tractability.
213A: Intermediate Probability and Statistics
Requisite: working knowledge of the differential and integral calculus of several variables, basic probability theory, and univariate mathematical statistics. Introduction to probability theory and hypothesis testing as applied to management. SAS programs used in this course and its sequels.
213C: Introduction to Multivariate Analysis
Requisites: working knowledge of the differential and integral calculus of several variables, matrices, basic probability theory, and univariate mathematical statistics. Introduction to use of multivariate models in management research to organize and represent information; interpretation of coefficients from multivariate exploratory models (e.g., principal axes and factor analysis models); survey of multivariate statistical procedures (e.g., multiple discriminate analysis, multivariate analysis of variance, canonical correlation, and confirmatory factor models).
216A: Simulation Modeling and Analysis
Requisites: probability theory, mathematical statistics, and analytical modeling. Focuses on the development of computer simulation models for managerial decision making under uncertainty or complex dynamics. Emphasis on simulation methodology such as design, validation, operating procedures, and interpretation of results. Application areas include finance, marketing, and production. Open to MBAs.
218A: Selected Topics in Decisions, Operations & Technology Management
Newly developing topics of interest to Ph.D. students. Past topics have included reliability and optimal maintenance theory, large-scale distribution/inventory systems, and Markovian decision processes under uncertainty. May be repeated for credit. S/U or letter grading.
242A: Models for Operations Planning, Scheduling, and Control
Requisite: Consent of instructor. Designed for Ph.D. students with some knowledge of mathematical programming and stochastic processes. The foundations of operations planning, scheduling, and control, with an emphasis on formal models and their applications. Aggregate planning, work force scheduling, inventory management, and detailed operations scheduling and control.
242B: Models for Operations Systems Design
Requisite: 210C. Designed for Ph.D. students. Survey of research literature on models for design of manufacturing and service systems, including long-range forecasting, operational economies, capacity, location, facilities, processes/technology, work, and work structures.
243B: Inventory Theory
Requisite: 210B. General discussion of inventory models, with emphasis on characterizing the form of optimal policies and efficient computational methods. Deterministic, stochastic, discrete-time, and continuous-time models.
243C: Scheduling Models for Intermittent Systems
Requisite: 242A. Scheduling models and results for single machine, flow shop, job shop, and resource-constrained project networks. Approaches include classical models, recent heuristic approaches, current research in coordinated interaction of computer models, and man/machine interaction.
243XYZ: Seminar in Decisions, Operations and Technology Management Systems (1-1-2)
Required of all DOTM Ph.D. students. Student, faculty, and guest speaker presentations of ongoing research. May be repeated for credit.
244XYZ: Research in Decisions, Operations & Technology Management (1-1-2)
Designed for Ph.D. students. Normally taken by first and second-years DOTM Ph.D. students. Survey of research literature in operations and technology management. Seminar reports dealing with special topics. May be repeated for credit with topic change.
245: Special Topics in Decisions, Operations and Technology Management
Studies of advanced subjects of current interest in DOTM. Emphasis on recent developments and application of specialized knowledge. Topics vary each term and have recently included: Strategy for Information Intensive Industries, Empirical Research in Operations Management, Analytical Methods of Operation Research, Introduction to Management in the Information Economy, and Models for Medical Management. May be repeated for credit with topic change. Each section is designed either for MBA or Ph.D. students.
245: Special Topics in Decisions, Operations and Technology Management: Dynamic Programming
Requisite: knowledge of markov chains and linear programming. The purpose of this course is to introduce students to dynamic programming (DP) as a general solution approach for problems that have an inter-temporal nature or lend themselves to be solved sequentially. The course covers the DP recursion or Bellman equation mostly for discrete time problems, including finite and infinite horizon as well as deterministic and stochastic transitions. The continuous time case is only covered for deterministic or Semi-Markovian settings. The course also covers a wide selection of seminal papers taken from the operations research and management science literature.
298D: Special Topics in Management
In-depth examination of problems or issues of current concern in management, with different topics offered each year. Recent topics have included: E-Business and Supply Chains, Electronic Commerce, Topics in Mathematical Programming, "Competition, Markets, and Power", and High-Tech Marketing. Each section is designed either for MBA or Ph.D. students.