The complexity of modern organizations necessitates the use of mathematical modeling techniques. Management Science and Operations Research are concerned with the application of mathematical models to decision problems in business, industry, government and other organizations.
Our PhD program offers a wide variety of courses in Management Science, Operations Research and Operations Management, exposing students to the latest methods, recent modeling approaches and current issues in the general areas of operations and logistics.
Sauder's Centre for Operations Excellence (COE) provides a focus for applied management science and business statistics activities at UBC. This centre offers PhD students the opportunity to work on significant applied projects.
The Management Science PhD program is intended for students with strong technical background who aspire to a career either in academia, industrial research, or as a high level staff member in government or industry. The Operations and Logistics Division, which offers the Management Science PhD program, has earned a justifiably high reputation as one of the best research departments in North America.
Its current research focus emphasizes Supply Chain Management and Health Care Management. Graduates from the Management Science PhD program are respected for both the quality and creativity of their research. Many of them hold positions at leading schools in Canada, the USA and worldwide. Others have taken up careers in a wide variety of organizations.
For more information
Michael KimProfessor, Management Science PhD Advisor
Program of study
Students are required to take the following courses (as well as a topics course, usually COMM 612 or 682, which contents varies from year to year to reflect current interests):
Focuses on significant applications of probabilistic (stochastic) models. Areas of application to be considered are inventory control, queuing and telecommunication systems. It will focus on applications of discrete time Markov chains and Poisson processes.
This course is a continuation of BAMS 501. This course will introduce and study some additional stochastic processes such as continuous-time Markov chain and renewal processes, so that students can use these processes to model and analyze some simple problems in queueing, inventory, telecommunication and some other operations management systems.
Optimization problems arise whenever one seeks to use limited available resources in the best possible way, to maximize profits, to minimize costs, or to find a “best” solution to a complex problem. Optimization applies to many functional fields of management, such as logistics and operations management, health care, accounting and finance; to several disciplines in science, such as computer science, mathematics, physics and biology; and to most fields in engineering.
The course will present the basic models and methods in (Constrained) Optimization, also known as Mathematical Programming. Applications to functional areas of business and related fields will be introduced in class and in homework assignments, and will be solved using appropriate computer software. The main objective of this course is to learn how to formulate a mathematical program, to solve it using appropriate tools, interpret the solutions and derive managerial insights relevant to the intended application.
The course presents the basic models and methods in Discrete Optimization, namely, Integer Programming and Network Optimization. The emphasis is on useful methodologies and their applications in production and operations management, supply chain management, transportation and logistics, project planning, as well as capital budgeting and investment planning involving discrete activities.
The module will address some basic ideas of decision analysis and how they can be extended into a more general Markov Decision Process (MDP) model. These methods will be applied to a wide range of disciplines including management science, economics, telecommunications, and computer science. The objectives of the course are to teach students to formulate and solve MDP models under several optimality criteria.
The module will address some basic ideas of infinite horizon Markov Decision Process (MDP) model. The objectives of the module are to teach students to formulate and solve infinite horizon MDP models under several optimality criteria.
This course deals with the many aspects of regression analysis - both conceptual and mathematical. Topics covered include the general linear model and its many ramifications; variance and covariance analysis; indicator variables; experimental design; applying the linear model; and econometric issues.
The course presents the basic theory and some methods for Constrained Optimization, also known as Mathematical Programming, covering its three main areas of Linear Programming, Nonlinear Programming, and Discrete Optimization. The emphasis is on the main theoretical and structural results, in particular on optimality conditions, duality, convexity (and its extensions), and post-optimality analysis. Some methods are presented, in particular the Simplex Method of Linear Programming (in part as a tool for proving theoretical results), and deterministic Dynamic Programming (as a general methodology).
These topics courses review the state of the art in selected areas of Management Science, Operations Research, and Production and Operations Management.
Students also take a Commerce Minor, consisting of three graduate courses in management, to supplement their more technical Management Science education. You select these and other elective courses with the guidance of the Management Science PhD Advisor or your research supervisor.
You write a Preliminary Examination in the beginning of your first summer. In your first two summers, you begin active research (Summer Research Internship) as a Research Assistant with a faculty member. Most students write their Comprehensive Examination after their second year of course-work and spend the remainder of the program working on their dissertation research.
Sample program sequence
A typical schedule for a Management Science PhD student may look as follows. Selection of elective and minor courses will depend on the interests and background of the student.
Year 1 Fall: BAMS 501, 502, 506, 508; COMM 581, COMM 693 (Research Methods)
Year 1 Spring: BAMS 517, 518; COMM 616; COMM 612 or 682; EPSE 606, or one elective or Commerce Minor course
Year 1 Early Summer: Preliminary Examination
Year 1 Summer: Summer Research Internship
Year 2 Commerce Minor courses; EPSE 606; second COMM 612 or 682 course; elective courses; research and preparation for the Comprehensive Exam
Year 2 Summer: Comprehensive Exam; Summer Research Internship
Year 3 Preparation and presentation of Thesis Proposal
Year 4 Preparation and defense of thesis