|YEAR||June - August||September - December||January - March||April - June|
Mathematical Methods in Economics
Operations Management I
Network Flows & Integer Programming
Operations Management II
MGMT PHD 201A,B,C
MGMTPHD 201A Probability, Statistics, and Computational Methods for Econometrics
Introduction to probabilistic, statistical, and computational tools needed for applied researchers in business fields. Probability theory, modes of convergence, hypothesis testing, Bayesian inference, R programming, linear algebra, numerical optimization, simulation methods, numerical integration
MGMTPHD 201B Theory and Application of Regression Analysis
Introduction to general regression analysis. Linear model, maximum likelihood and asymptotic tests, endogeneity, instrumental variables, differences-in-differences, regression-discontinuity design, propensity score matching, limited dependent variable models, introduction to panel data.
MGMTPHD 201C Time-Series Analysis
Introduction to time-series methods in analysis of business data. Basics of time series, optimal prediction, multiple equation time-series models, generalized method-of-moments, volatility modeling and nonnormalities, dynamic factor models.
MGMT PHD 258
Basics of single-person decision theory and introduction to non-cooperative game theory. Examination in some detail of von Neumann/Morgenstern expected utility theory. Other topics in decision theory include subjective expected utility theory and departures from expected utility behavior.
Network Flows & Integer Programming
MGMT PHD 231
Survey course to (1) lay foundations for more advanced study of graphs, network flow models, and integer programming models and their applications, (2) establish connections between these technical foundations and real problems drawn from many areas of management, and (3) build professional skills needed to apply these tools
Models for Operations Planning, Scheduling and Control
MGMT PHD 241A
Scheduling Models for Intermittent Systems
MGMT PHD 241B
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.
MGMT PHD 257
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 being solved sequentially. The course will cover the DP recursion or Bellman equation mostly for discrete time problems. That includes finite and infinite horizon as well as deterministic and stochastic transitions. The continuous time case will only be covered for deterministic or Semi-Markovian settings.
Stochastic Modeling with Applications to Telecommunication Systems
Stochastic processes as applied to study of telecommunication systems, traffic engineering, business, and management. Discrete-time and continuous-time Markov chain processes. Renewal processes, regenerative processes, Markov-renewal, semi-Markov and semiregenerative stochastic processes. Decision and reward processes. Applications to traffic and queueing analysis of basic telecommunications and computer communication networks, Internet, and management systems.
Telecommunication Switching and Queueing Systems
Modeling, analysis, and design of queueing systems with applications to switching systems, communications networks, wireless systems and networks, and business and management systems. Modeling, analysis, and design of Markovian and non-Markovian queueing systems. Priority service systems. Queueing networks with applications to computer communications, Internet, and management networks.
Basic graduate course in linear optimization. Geometry of linear programming. Duality. Simplex method. Interior-point methods. Decomposition and large-scale linear programming. Quadratic programming and complementary pivot theory. Engineering applications. Introduction to integer linear programming and computational complexity theory.
Introduction to convex optimization and its applications. Convex sets, functions, and basics of convex analysis. Convex optimization problems (linear and quadratic programming, second-order cone and semidefinite programming, geometric programming). Lagrange duality and optimality conditions. Applications of convex optimization. Unconstrained minimization methods. Interior-point and cutting-plane algorithms. Introduction to nonlinear programming.
Optimization Methods for Large-scale Systems
First-order algorithms for convex optimization: subgradient method, conjugate gradient method, proximal gradient and accelerated proximal gradient methods, block coordinate descent. Decomposition of large-scale optimization problems. Augmented Lagrangian method and alternating direction method of multipliers. Monotone operators and operator-splitting algorithms. Second-order algorithms: inexact Newton methods, interior-point algorithms for conic optimization.
Mathematical Methods in Economics
Examination of mathematical methods used in graduate-level courses in microeconomics, macroeconomics, and quantitative methods. Topics include real analysis, linear algebra and matrices, calculus of many variables, static optimization, convex analysis, and dynamics and dynamic optimization.
These seminars are hosted by your Area of study and closed to the public; distinguished faculty from other universities present their latest papers and findings.
On June 6, 2014, Professor Georgia Perakis of MIT’s Sloan School of Management presented her paper “The Role of Optimization in Promotion Planning for Supermarkets.” The paper and talk centered on the Promotion Optimization Problem (POP), a challenging problem as the retailer needs to decide which products to promote, what is the depth of price discounts and finally, when to schedule the promotions. Introduced and discussed was an optimization formulation that captures several important business requirements as constraints. The formulation proposed solves fast in practice using actual data from a grocery retailer and that the accuracy is high. Research included calibrating the models using actual data and determined that retailers can improve profits by 3% just by optimizing the promotion schedule and up to 5% by slightly modifying some business requirements.
Recently, DOTM was also pleased to host Professor Francis de Vericourt from INSEAD. He presented his paper “Financing Capacity Investment Under Demand Uncertainty.” The paper studies the interplay between the operational and financial facets of capacity investment, and findings include that when higher demand realizations are more indicative of high effort, debt financing is optimal for any given capacity level. In this case, the optimal capacity is never below the efficient capacity level but sometimes strictly above that level.
Our student-run seminars exist exclusively for Anderson and Economics Ph.D. students. They present their current research and receive feedback. With no faculty in attendance, your peers critique your work, becoming invaluable, supportive colleagues and friends in the process..
Job Market Papers
These seminars provide opportunity to defend your work, receive research career feedback and influence others’ work-lives. The toughest audiences you will ever face, the experience is all about preparing you for your professional life and positioning you for candidacy as a faculty member at one of the world’s highest caliber institutions.