Introduction to industrial engineering concepts. Fundamentals of systems analysis and modeling. Basics of production and service systems. Computer and programming applications of several industrial engineering topics. Hands-on experience for industrial engineering subjects in team projects

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.

Financial accounting principles and cost systems for engineering economic analyses. Cost-volume-profit analyses, discounted cash flow and budgeting techniques.

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 modeling concepts and optimization; setting upoptimization models from problem description; linear programming problem formulation; simplex method, duality and sensitivity analysis; applications of mathematical programming in engineering and management with computer implementations.

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 of simulation models to analyze the behavior of complex stochastic systems. Modeling time and randomness, model validation. Generation of stochastic inputs, random variate generation. Implementation of models arising from case studies via simulation languages and software. Output analysis, variance reduction techniques. Monte Carlo and Quasi Monte Carlo 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

Introduction to technological and conceptual aspects of information systems; data and information modeling systems, design and analysis of modular information systems, workflow modeling and project management methodology, models for information systems process development and implementation, post-implementation of IT systems, information systems examples including materials requirement planning, enterprise resource planning and supply chain management.

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.

Constructive heuristics; improving heuristics; metaheuristics: simulated annealing, genetic algorithms, tabu search, scatter search, path relinking, ant colony

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.

The basic tools and concepts of politics, political systems, and political science; an overview of the basic terminology and theories of political science so as to enable students to understand the functioning of different political systems; a systematic understanding of political institutions and dynamics as a basis for an adequate analysis of global problems, from economic development to security to the environment.