The aim of the course is to give qualified Industrial Engineering students a unique opportunity to serve as undergraduate teaching assistants (TA) as a part of their undergraduate experience. Students are responsible for running review and problem sessions, holding office hours and supervising laboratories for Industrial Engineering core and area courses.

The aim of the course is to give qualified Industrial Engineering students a unique opportunity to serve as undergraduate teaching assistants (TA) as a part of their undergraduate experience. Students are responsible for running review and problem sessions, holding office hours and supervising laboratories for Industrial Engineering core and area courses.

Fundamentals of logic, mathematical induction, basic set theory, relations and functions, fundamental principles of counting, inclusion-exclusion principles, basic graph theory, trees, algorithms for basic industrial engineering and operations research problems on graphs and networks.

A broad introduction to scientific computing, linear algebra and scientific computing libraries; formulating optimization problems for real-life scenarios and algebraic representations of optimization models; introduction to commercial optimization solvers; solving linear programming, (mixed) integer linear programming, unconstrained nonlinear programming, quadratic programming, and quadratically constrained quadratic programming models using optimization solvers; formulating statistical models as optimization problems and solving them using optimization solvers.

Introduction to inventory management, deterministic economic quantity models and extensions. Stochastic continuous-review and periodic-review models. Markov chains and Markov processes. Introduction to queueing systems and the Poisson process. Markovian queues, networks and management of queueing systems. Markov decision models and applications. Probabilistic dynamic programming and algorithmic solution methods.

Introduction to modeling with integer variables and integer programming; network models, dynamic programming; convexity and nonlinear optimization; applications of various optimization methods in manufacturing, product design, communications networks, transportation, supply chain, and financial systems.

Facilities design process; strategic facilities planning, product, process, and schedule design, flow, space, and activity relationships, personnel requirements; material handling principles, equipment, unit load concept; facility layout, types, procedures, computer-aided tools; warehousing, order picking, automated storage/retrieval systems; quantitative models for facilities planning; evaluating, selecting, preparing, presenting, implementing, and maintaining the facilities plan.

Network flow models and optimization problems. Algorithms and applications. Minimum spanning tree problem. Shortest path problems. Maximum flow problems, minimum cuts in undirected graphs and cut-trees. The minimum cost network flow problem. Matching problems. Generalized flows. Multicommodity flows and solution by Lagrangean relaxation, column generation and Dantzig-Wolfe decomposition. Network design problems including the Steiner tree problem and the multicommodity capacitated network design problem; their formulations, branch-and-cut approaches and approximation algorithms.

Tools, techniques, and skills needed to analyze decision-making problems characterized by uncertainty, risk, and conflicting objectives. Methods for structuring and modeling decision problems and applications to problems in a variety of managerial decision-making contexts. Structuring decision problems: Decision trees, model building, solution methods and sensitivity analysis; Bayes' rule, the value of information and using decision analysis software. Uncertainty and its measurement: Probability assessment. Utility Theory: Risk attitudes, single- and multiattribute utility theory, and risk management. Decision making with multiple objectives.

Price-response function and incremental costs. Pricing in a single or a segmented market. Pricing under supply constraints. Identifying revenue management opportunities. Capacity allocation. Network management. Overbooking. Markdown management. Customized pricing. Customer acceptance

Application and development of mathematical modeling tools for the analysis of strategic, tactical, and operational supply-chain problems. Mathematical programming formulations for integrated planning of capacity and demand in a supply chain. Planning and managing inventories in multi-level systems, centralized versus decentralized control of supply chain inventories. Models and algorithms for transportation and logistics systems design and analysis. Supply chain coordination issues and achieving coordination through contracts. The role of information technology and enterprise resource planning (ERP) and Advanced Planning and Optimization software.

A capstone design course where students apply engineering and science knowledge in an industrial engineering design project proposed by companies from different sectors. Development, design, implementation and management of a project in teams under realistic constraints and conditions. Emphasis on communication, teamwork and presentation skills.

Convex analysis, optimality conditions, linear programming model formulation, simplex method, duality, dual simplex method, sensitivity analysis; assignment, transportation, and transshipment problems.

The basic theory of the Poisson process, renewal processes, Markov chains in discrete and continuous time, as well as Brownian motion and random walks are developed. Applications of these stochastic processes are emphasized by examples, which are drawn from inventory and queueing theory, reliability and replacement theory, finance, population dynamics and other biological models.

Network flow models and optimization problems. Algorithms and applications. Minimum spanning tree problem. Shortest path problems. Maximum flow problems, minimum cuts in undirected graphs and cut-trees. The minimum cost network flow problem. Matching problems. Generalized flows. Multicommodity flows and solution by Lagrangean relaxation, column generation and Dantzig-Wolfe decomposition. Network design problems including the Steiner tree problem and the multicommodity capacitated network design problem; their formulations, branch-and-cut approaches and approximation algorithms.

Tools, techniques, and skills needed to analyze decision-making problems characterized by uncertainty, risk, and conflicting objectives. Methods for structuring and modeling decision problems and applications to problems in a variety of managerial decision-making contexts. Structuring decision problems: Decision trees, model building, solution methods and sensitivity analysis; Bayes' rule, the value of information and using decision analysis software. Uncertainty and its measurement: Probability assessment. Utility Theory: Risk attitudes, single- and multiattribute utility theory, and risk management. Decision making with multiple objectives.

Price-response function and incremental costs. Pricing in a single or a segmented market. Pricing under supply constraints. Identifying revenue management opportunities. Capacity allocation. Network management. Overbooking. Markdown management. Customized pricing. Customer acceptance

Application and development of mathematical modeling tools for the analysis of strategic, tactical, and operational supply-chain problems. Mathematical programming formulations for integrated planning of capacity and demand in a supply chain. Planning and managing inventories in multi-level systems, centralized versus decentralized control of supply chain inventories. Models and algorithms for transportation and logistics systems design and analysis. Supply chain coordination issues and achieving coordination through contracts. The role of information technology and enterprise resource planning (ERP) and Advanced Planning and Optimization software.

A series of lectures given by faculty or outside speakers. Participating students must also make presentations during the semester.

Evolution of the modern international system, with particular emphasis on developments since World War II, basic theories and applications of salient issues in international politics such as international conflict and cooperation, alignments, nationalism, and forces of change.

An historical analysis of great political ideas as put forth by ancient and modern philosophers and political theorists such as Plato, Aristotle, Machiavelli, Rousseau and Marx. Intellectual debates on the foundational questions of politics (forms of government, the relationship of the individual to the state, justice and morality).

This undergraduate seminar critically explores a variety of political, social, and economic processes through a gendered perspective. The class revisits issues of politics and political economy by focusing on various inequalities that govern the lives of men and women in their everyday lives. The course material is organized so that we discuss themes such as, but not limited to, nation-state formation, citizenship, labor, and development.

Theories of conflict and aspects of international security, including alliances, international organizations, ethnic and national conflict, and proliferation of weapons of mass destruction.

Comprehensive introduction to the comparative study of Soviet and Post-Soviet Russian and Eurasian politics, including political parties and the parliament, ethnic politics and nationalism, law, media, civil-military relations, economy, demography, and foreign policy.