Decision sciences doctoral students are expected to enter the program with an acceptable background in mathematics. Students who lack this knowledge must take the appropriate course(s) in mathematics and statistics.
Depending on their backgrounds, students may also be required to take one of the doctoral statistics sequences within the Kelley School of Business. Students will also be required to take the basic economics sequence: economic modeling, game theory, and econometrics. Students without prior teaching experience in the U.S. will take one short course (X630) on teaching prior to teaching their first course.
Decision sciences doctoral students complete 18 credit hours of coursework within their major area. Because PhD students in this area are part of the Decision Sciences/Operations Management Doctoral Consortium, this coursework consists of both decision sciences and operations management courses. Specifically, students take the basic operations research sequence and the basic operations management sequence in their first two years. In addition to the basic courses, decision sciences majors generally also take topics courses. As with other business majors, students supplement the program with minor courses (9 credit hours), and methodology and analysis courses (9 credit hours).
Required Courses
15 weeks
Prerequisite(s): None
This course covers linear optimization. In particular, it focuses on theory, solution methods, and formulations for linear optimization problems. Topics include linear programming, including the simplex and interior point methods, duality, and network flow problems.
15 weeks
Prerequisite(s): None
This course covers integer and nonlinear optimization. It focuses on theory, solution methods, and formulations for these problems. Topics include integer programming, non-linear programming (unconstrained and constrained), and stochastic programming.
15 weeks
Prerequisite(s): None
The course focuses on establishing a solid understanding on probability and on how to use it to build applied models. It includes topics such as conditional probability and expectation, transforms, stochastic orders, counting processes, renewal theory, regenerative processes (including the M/G/1 queue), and semi-Markov processes. Students will apply these models to inspection and reward problems, open and closed queueing networks, and optimization problems. This course will also cover the basics of discrete-event simulation.
15 weeks
Prerequisite(s): None
This course provides a survey of key research themes in the area of supply chain management and distribution. Topics include multi-echelon inventory (stochastic and guaranteed service level models), risk pooling, postponement, transshipments, supply chain disruption, yield uncertainty, process flexibility, the bullwhip effect, and supply chain contracts.
15 weeks
Prerequisite(s): None
This course provides an overview of quantitative models and techniques of inventory management that support the management of production and distribution. Topics include forecasting, the EOQ model and its variations (quantity discounts, power-of-two policies, planned backorders), the Wagner-Within algorithm, and stochastic inventory models (continuous review (r,Q) policies, periodic review base-stock policies, and (s,S) policies).
15 weeks
Prerequisite(s): None
This course provides a survey of Operations & Supply Chain topics drawn from the current literature.
15 weeks
Prerequisite(s): None
This course focuses on service management and process design, emphasizing operations management issues and decision making methods. Topics include service management frameworks, service design and process selection, service quality, service operations and the behavioral sciences, labor scheduling and flexibility, capacity planning, and revenue management.
15 weeks
Prerequisite(s): None
The course introduces the fundamental knowledge in stochastic processes. It includes topics such as Poisson processes, discrete- and continuous-time Markov chains, birth-and-death processes. Applications include phase-type distributions and Markovian queues. Students will represent Markov chains in matrix form and solve for transient and steady-state distributions using MATLAB, Python, or similar software. This course will also cover the basics of random variate generation and Monte Carlo simulation.
15 weeks
Prerequisite(s): None
This course provides an introduction to the formulation and analysis of dynamic optimization problems. Topics include, convexity and concavity, K-convexity, stochastic dominance, dimensionality reduction in dynamic programs, and Lippman’s transformation.
8 weeks
Prerequisite(s): None
The topics included in this course varies at the time of the offering. Usually, a sub-field such as humanitarian logistics, health care OM, contracts in supply chain management, sustainable operations, and so forth, is explored in depth.
