PhD Modules Offered by the Decision Sciences Department
BDC6111 Foundations of Optimization (4 MC)
This course will cover important topics in optimization theory including linear, network, discrete, convex, conic, stochastic and robust optimization. It will focus on methodology, modeling techniques and mathematical insights.
BDC6112 Stochastic Processes (4 MC)
This module aims to provide PhD students with a rigorous introduction to stochastic processes. Examples showing how various concepts and techniques in stochastic processes can be adapted to solve real world problems will be discussed.
BDC6113 Foundations of Inventory Management (4 MC)
This course will provide an in-depth study of a variety of production and inventory control planning problems, the development of mathematical models corresponding to these problems, approaches to characterize solutions, and algorithm designs for finding solutions.
We will cover deterministic as well as stochastic inventory models. Although many of the topics we will cover are of great interest to managers, our focus will not be on practice but on theory.
BDC6114 Logistics and Supply Chain (4 MC)
The objective of this course is to expose students to the issues that need to be considered in designing and operating logistics and supply chains. We will start with an introduction including definition of logistics and supply chain management, key supply chain costs and metrics, and fundamental issues and trade-offs in supply chain management. We will then discuss the interactions between stages in a supply chain, double marginalization and contracts for supply chain coordination, strategic alliances and incentive alignment, channels of distribution, coordinating distribution strategies, pricing/promotions. We will also discuss supply chain planning, facility location models, and vehicle routing models.
BBP6793 Economic Modelling for Management Research (4 MC)
This course introduces contemporary economic analytical tools and concepts to strategy
Ph.D. students. Model economic frameworks (e.g. game theory and information economics) have been pervasively applied to model industrial competition and firms organizational strategies. The extensive literature has yielded important insights to a wide array of managerial issues. Economic modelling skills could significantly benefit strategy research, as formal analysis allows a researcher to substantiate research conjectures from mere intuition, to generate plausible hypotheses and to pave a solid foundation for further empirical work. This course aims to equip strategy students with cutting-edge economic modelling skills. The course starts by presenting basic game theory and information economics models and concepts. The course then introduces their applications in diverse

managerial scenarios, such as product competition, pricing strategies, R&D competition and cooperation, advertising, product differentiation, merger and acquisition, firm theory, etc.
BDC6211 Convex Optimization (2 MC)
Convex optimization is one of the most important models in management sciences. It is also a basis for further study in optimization, both in theory and in computation. This module will provide such a basis, while put enough attention on business applications. The objective is to introduce the fundamental ideas and concepts in convex optimization to PhD students.
Focus will be on theory and analysis of algorithm for problems in this area.
BDC6212 Discrete Optimization and Applications (2 MC)
Discrete optimization is the study of problems where the goal is to find an optimal arrangement from among a finite set of possible arrangements. Many applications in business, industry, and computer science lead to such problems, and we will touch on the theory behind these applications. We cover several important concepts and applications in this area. Topics covered include: (1) Matroid and Greedy Algorithm; (2) Network Flow; (3)
Matching; (4) Polyhedral Methods, including Cutting Plane and Column Generation; (5) Submodular minimization. We discuss how these approaches can be used to tackle problems arising in operations and other diverse areas.
BDC6221 Stochastic Modelling and Optimization (4 MC)
Stochastic models are used extensively to analyze and optimize in a wide variety of applications including business operations, economic systems, finance and engineering. This module provides the core knowledge for graduate students specializing in operation management and management sciences. Topic covered includes dynamic programming, stochastic ordering and their applications.
BDC6222 Simulation Modeling and Analysis (2 MC)
The course introduces students to fundamental simulation theories, modeling and analysis skills. The course covers building valid and credible models, random-number generator and random-variate generation, modeling and design of experiment, and analysis of simulation output. The primary attention will be given to modeling problems in business, service operations and supply chain systems, by means of discrete-event simulation. Using spreadsheets and simulation software to build simulation models, conduct experiments and data analysis is essential in this course.
BDC6241 Operations Management (2 MC)
We cover the following topics in Operations Management not already included in the other
OM-related modules: Operations Strategy, Service Operations, New Product Development and Project Management. We aim to provide an overview of the relevant literature as well as expose the students to the new developments and readings in these topics.

BDC6242 Operations and Process Design (2 MC)
The course objective is to introduce students to the research issues associated with key programs and initiatives undertaken by organizations to improve their operational capabilities and performance. Hence, this module focuses on the design, implementation and management of operations processes and systems. These processes and systems include programs and initiatives adopted by organizations to improve its operational and logistical performance. Topics covered include lean management, six sigma, TQM, enterprise resource planning (ERP), JIT, business process re-engineering (BPR), and knowledge management systems. The module follows a seminar-cum-discussion format.
BDC 6401 Decision Sciences Pro-seminar (4 MC)
The primary objective of this module is to introduce entering doctoral students to the current research interests of the decision sciences (DS) faculty members. Each week, a DS professor will present and his/her current research agenda. Readings will be assigned and students are required to submit a short critique every week to the respective professor who is in charge of that week’s topic, unless otherwise noted.
BDC6218/9 Seminars In Optimization (2 MC)
This is an advanced PhD-level module on Optimization that builds on the foundations developed in BDC6111. Specific content of this module will depend on student and faculty interests. This module will provide an opportunity for students to be exposed to cuttingedge research topics related to Optimization that are not otherwise included in the curriculum.
BDC6228/9 Seminars In Stochastic Processes (2 MC)
This is an advanced PhD level module on stochastic modelling and optimization developed in
BDC 6221. This module’s purpose is to systematically train the graduate students how to use the Renewal Processes, Stochastic Dynamic Programming, Queueing Theory and
Stochastic Comparisons to model the practical problems. Further, in this module, we also teach the graduate students how to use the techniques developed in the above areas to rigorously analyze the models.
BDC6238/9 Seminars In Supply Chain Management & Logistics (2 MC)
This is an advanced PhD-level module on Supply Chain Management & Logistics that builds on the foundations developed in BDC6113 and BDC6114. Specific content of this module will depend on student and faculty interests. This module will provide an opportunity for students to be exposed to current topics related to Supply Chain Management & Logistics that is not otherwise included in the curriculum.
BDC 6248/9 Seminars In Operations Management (2 MC)
This is an advanced PhD level module on operations management that builds on the foundation courses. Specific content of this module will depend on student and faculty interests. This module will provide an opportunity for students to be exposed to cuttingedge research topics related to stochastic modeling or stochastic optimization that are not otherwise included in the curriculum.