Courses

INDR 100 / INTRODUCTION TO INDUSTRIAL ENGINEERING

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
Credits: 3

INDR 201 / DISCRETE MATHEMATICAL STRUCTURES

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.
Credits: 3
Prerequisite: MATH. 106 or consent of the instructor

INDR 202 / ENGINEERING ECONOMICS

Financial accounting principles and cost systems for engineering economic analyses. Cost-volume-profit analyses, discounted cash flow and budgeting techniques.
Credits: 3

INDR 252 / APPLIED STATISTICS

Basic parametric statistics such as estimation, confidence intervals, and hypothesis testing. Distribution fitting, goodness of fit tests. Independence tests and contingency tables. Simple linear regression and correlation analysis. Nonlinear and multiple regression, analysis of categorical data. Industrial engineering applications in quality control and demand forecasting. Statistical software packages and computer implementations.
Credits: 4
Prerequisite: ENGR. 200 or consent of the instructor

INDR 262 / INTRODUCTION TO OPTIMIZATION METHODS

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.
Credits: 4
Prerequisite: MATH. 107 or MATH 106 or Consent of the instructor

INDR 291 / SUMMER PRACTICE I

A minimum of 20 working days of training in an industrial summer practice program after the completion of second year. The training is based on the contents of the "Summer Practice Guide Booklet" prepared by each engineering department. Students receive practical knowledge and hands-on experience in an industrial setting.
Credits: 0
Prerequisite: INDR 100 and (INDR 252 or INDR 262)

INDR 343 / STOCHASTIC MODELS

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.
Credits: 3
Prerequisite: (ENGR. 200 and INDR. 262) or consent of the instructor

INDR 344 / MODELING AND SIMULATION

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.
Credits: 4
Prerequisite: INDR. 252 or consent of the instructor

INDR 363 / MATHEMATICAL PROGRAMMING

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.
Credits: 3
Prerequisite: INDR. 262 and INDR. 201

INDR 371 / OPERATIONS AND FACILITIES DESIGN

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.
Credits: 3
Prerequisite: INDR. 262 or consent of the instructor

INDR 372 / PRODUCTION PLANNING AND CONTROL

Quantitative models for decision-making with focus on tactical and operational decisions in manufacturing environments. Aggregate planning, inventory control, forecasting, project management, production scheduling, manpower and capacity planning, location and layout planning, manufacturing resource planning (MRP) and just-in-time (JIT) systems.
Credits: 3
Prerequisite: (INDR. 262 and INDR. 343) or consent of the instructor

INDR 391 / SUMMER PRACTICE II

A minimum of 20 working days of training in an industrial summer practice program after the completion of third year. The training is based on the contents of the "Summer Practice Guide Booklet" prepared by each engineering department. Students receive practical knowledge and hands-on experience in an industrial setting.
Credits: 0
Prerequisite: INDR 291 or ENGR 291

INDR 420 / NETWORK MODELS AND OPTIMIZATION

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.
Credits: 3
Prerequisite: INDR. 262 or consent of the instructor

INDR 430 / DECISION ANALYSIS

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.
Credits: 3
Prerequisite: (ENGR. 200 or ENGR. 201 or MATH. 201) or consent of the instructor

INDR 440 / PROJECT MANAGEMENT

Strategy with projects; integration of organization with projects; defining the project; estimating times and costs; developing a network plan; LP approach for CPM; PERT; scheduling resources; mathematical models for resource allocation; reducing project duration; mathematical model for crashing; progress and evaluation; control process; project closure audit process; international projects.
Credits: 3
Prerequisite: INDR. 262

INDR 460 / OPERATIONS RESEARCH APPLICATIONS

Modeling and analysis of large-scale and complex systems; mathematical model building; the solution of these models with computational tools and post-optimality analysis for decision-making problems arising in a wide range of real-life applications; building effective linear, nonlinear, integer, network and stochastic programming models; using optimization software for the solution of these models and interpretation of the computer output; applications in transportation and logistics planning, data mining, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning.
Credits: 3
Prerequisite: (INDR. 262 and INDR. 363)

INDR 471 / SERVICE OPERATIONS ANALYSIS

Distinctions of service operations. Measuring and benchmarking productivity: Data Envelopment Analysis (theory and applications). Service Quality. Capacity management and design in services. Capacity-constrained services and demand management (revenue management and optimization). Workflow analysis,productivity and quality management,response time (queuing) analysis. Customer relationship and loyalty issues (data-mining). Applications of analysis tools to several sectors such as health care, call centers, financial services, hotels and airlines.
Credits: 3
Prerequisite: (INDR. 262 and INDR. 343) or consent of the instructor

INDR 473 / FINANCIAL ENGINEERING

Investments and cash flows, present value and internal rate of return; fixed income securities, yield, duration and immunization; portfolio optimization, mean-variance models, Capital Asset Pricing Model and Arbitrage Pricing Theory; forwards, futures, swaps and risk hedging; pricing derivative securities and options, binomial market models, continuous market models and Black-Scholes equation.
Credits: 3
Prerequisite: ENGR. 200

INDR 481 / INFORMATION SYSTEMS

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.
Credits: 3

INDR 483 / SUPPLY CHAIN MODELING AND ANALYSIS

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.
Credits: 3
Prerequisite: (ENGR. 200 and INDR. 262 and INDR. 372) or consent of the instructor

INDR 484 / LOGISTICS MANAGEMENT

Formulation of integer and combinatorial optimization problems Introduction to logistics systems; logistics network design, location models; warehouse design, tactical decisions, operational decisions; transportation management; planning and managing freight transportation; fleet management, vehicle routing problem.
Credits: 3
Prerequisite: INDR. 262

INDR 486 / HEALTH AND HUMANITARIAN LOGISTICS

Principles of logistics and supply chain operations in the humanitarian context and health care systems. Broad understanding of how Operations Research techniques can be used in humanitarian operations and response functions by case studies. Mathematical modeling and solution of decision making problems in disaster mitigation, response and recovery operations that involve planning and design functions. Logistic problems arising in the healthcare sector such as ambulance assignment and routing in medical emergency response, blood collection and inventory management. Location of public service facilities such as hospitals and fire stations for long-term development.
Credits: 3

INDR 491 / INDUSTRIAL ENGINEERING DESIGN I

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.
Credits: 4
Prerequisite: (INDR. 344 and INDR. 372) or consent of the instructor

INDR 492 / INDUSTRIAL ENGINEERING DESIGN II

A capstone design project on an industrially relevant problem. Students work on teams in consultation with various faculty and industrial members.
Credits: 3

INDR 501 / OPTIMIZATION MODELS AND ALGORITHMS

Convex analysis, optimality conditions, linear programming model formulation, simplex method, duality, dual simplex method, sensitivity analysis; assignment, transportation, and transshipment problems.
Credits: 3

INDR 502 / LOGISTICS AND SUPPLY CHAIN SYSTEMS

Introduction to the concepts and terminology of logistics and supply chain management. Examination of components of logistics and supply chain systems such as purchasing, storage, production, inventory, and transportation systems. Analysis of interactions and trade-offs among these components using mathematical models and quantitative techniques.
Credits: 3
Prerequisite: INDR. 501 or consent of the instructor

INDR 503 / STOCHASTIC MODELS AND THEIR APPLICATIONS

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.
Credits: 3

INDR 504 / ADVANCED ENGINEERING MATERIALS MANUFACTURING

Advanced Engineering Material Manufacturing Processes will be studied for (i) metals and (ii) plastics and composites. Material removal, addition, and change of form processes will be studied for metals. In the plastics and composites part, similarities/differences, advantages/disadvantages, and proper selection of manufacturing processes such as Injection Molding, Compression Molding, Extrusion, Sheet Forming, Tow Placement, Pultrusion, Liquid Molding, Filament Winding, Pultrusion and Autoclave Processing will be illustrated with applications from aerospace, automotive, biomedical, sporting goods and civil infrastructure industries. Issues and their solutions with in-situ sensing and on- and off-line control will be studied with examples.
Credits: 3

INDR 505 / MANUFACTURING SYSTEMS

This course will cover the basic concepts and techniques in hierarchical design, planning, and control of manufacturing systems. Topics include flow line and assembly systems, group technology and cellular manufacturing, just-in-time, flexible manufacturing systems.
Credits: 3

INDR 506 / COMPUTER INTEGRATED MANUFACTURING AND AUTOMATION

This course introduces Computer Aided Design and Manufacturing (CAD/CAM) Systems, Computer Numerical Control (CNC) Machine Tools, Modern Sensors in Manufacturing, Machining Processes, Rapid Prototyping, and Fundamentals of Industrial Robotics.
Credits: 3

INDR 508 / DISCRETE EVENT SIMULATION

Topics on distribution fitting and generating random numbers and random variates will be covered as well as the statistical analysis of simulation output including some well-known analysis methods and variance reduction techniques. Recent developments in the area will also be discussed.
Credits: 3
Prerequisite: INDR. 503 or consent of the instructor

INDR 510 / MATHEMATICAL STATISTICS

Review of descriptive statistics, importants populations statistics and their distributions. Point estimation, estimations methods and minimum-variance unbiased estimators. Testing hypothesis, Neyman-Pearson lemma and likelihood ratio tests. Estimation and testing in linear regression modes. Analysis of variance models. Nonparametric statistics methods. Bayesian testing and analysis.
Credits: 3
Prerequisite: INDR. 252 or consent of the instructor

INDR 511 / ADVANCED OPTIMIZATION METHODS

Combinatorial optimization, structure of integer programs, pure integer and mixed integer programming problems, branch and bound methods, cutting plane and polyhedral approach, convexity, local and global optima, Newton-type, and conjugate gradient methods for unconstrained optimization, Karush-Kuhn-Tucker conditions for optimality, algorithms for constrained nonlinear programming problems, applications in combinatorial and nonlinear optimization.
Credits: 3
Prerequisite: INDR. 501 or consent of the instructor

INDR 513 / ADVANCED STOCHASTIC PROCESSES

Brief review of basic processes like Poisson, Markov and renewal processes; Markov renewal processes and theory, regenerative and semi-regenerative processes; random walk, Wiener process and Brownian motion; martingales; stochastic differential equations and integrals; applications in queueing, inventory, reliability and financial systems.
Credits: 3
Prerequisite: INDR. 503 or consent of the instructor

INDR 520 / NETWORK MODELS AND OPTIMIZATION

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.
Credits: 3
Prerequisite: INDR. 262 or consent of the instructor

INDR 530 / DECISION ANALYSIS

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.
Credits: 3
Prerequisite: ENGR. 200 or consent of the instructor

INDR 540 / LOCATION THEORY

Analysis of selected models, algorithms, and applications from location theory. Study of deterministic and stochastic problems in continuous and discrete space. Capacitated and uncapacitated facility location. Covering problems. Center and median problems. The quadratic assignment problem and facility layout. Location and routing. Transportation of hazardous materials. Flow-interception. Voting and competitive location problems.
Credits: 3
Prerequisite: (INDR. 252 and INDR. 262) or consent of the instructor

INDR 560 / LARGE SCALE OPTIMIZATION

Methods for the solution of complex real world problems modeled as large-scale linear, nonlinear and stochastic programming, network optimization and discrete optimization problems. Solution methods include Decomposition Methods: Benders's, Dantzig-Wolfe, Lagrangian Methods; Meta-heuristics: Local search, simulated annealing, tabu search, genetic algorithms; Constraint Programming. Applications in transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning.
Credits: 3
Prerequisite: INDR. 501 or consent of the instructor

INDR 562 / INTEGER AND COMBINATORIAL OPTIMIZATION

Formulation of integer and combinatorial optimization problems. Optimality conditions and relaxation. Polyhedral theory and integer polyhedra. Computational complexity. The theory of valid inequality, strong formulations. Duality and relaxation of integer programming problems. General and special purpose algorithms including branch and bound, decomposition and cutting-plane algorithms.
Credits: 3
Prerequisite: INDR. 501 or consent of the instructor

INDR 564 / DYNAMIC PROGRAMMING

Theory and practice of dynamic programming, sequential decision making over time; the optimal value function and Bellman's functional equation for finite and infinite horizon problems; Introduction of solution techniques: policy iteration, value iteration, and linear programming; General stochastic formulations, Markov decision processes; application of dynamic programming to network flow, resource allocation, inventory control, equipment replacement, scheduling and queueing control.
Credits: 3
Prerequisite: (INDR. 501 and INDR. 503) or consent of the instructor

INDR 566 / SCHEDULING

Introduction to scheduling: examples of scheduling problems, role of scheduling, terminology, concepts, classifications; solution methods: enumerative methods, heuristic and approximation algorithms; single machine completion time, lateness and tardiness models; single machine sequence dependent setup models; parallel machine models; flow-shop models; flexible flow-shop models; job-shop models; shifting bottleneck heuristic; open-shop models; models in computer systems; survey of other scheduling problems; advanced concepts.
Credits: 3

INDR 568 / HEURISTIC METHODS

Constructive heuristics; improving heuristics; metaheuristics: simulated annealing, genetic algorithms, tabu search, scatter search, path relinking, ant colony
Credits: 3
Prerequisite: INDR. 501 or consent of the instructor

INDR 570 / QUEUEING THEORY

Markovian queues: M/M/1, M/M/C, M/M/C/K systems and applications. Phase-type distributions and matrix-geometric methods: PH/PH/1 systems. Queueing networks: reversibility and productform solutions. General arrival or service time distributions: embedded Markov Chains, M/G/1 and G/M/c queues, G/G/1 queues and the Lindley recursion, approximations. Stochastic comparisons of queues: stochastic orders, sample path properties.
Credits: 3
Prerequisite: INDR. 503 or consent of the instructor

INDR 572 / RELIABILITY THEORY

Basic concepts and definitions of system reliability. Series, parallel, k-out-of n systems. Structure functions, coherent systems, min-path and min-cut representations. System reliability assessment and computing reliability bounds. Parametric families of distributions, classes of life distributions and their properties. Shock and wear models. Maintenance, replacement and repairmodels. Current issues on stochastic modelling of hardware and software reliability.
Credits: 3
Prerequisite: INDR. 503 or consent of the instructor

INDR 573 / FINANCIAL ENGINEERING

Investments and cash flows, present value and internal rate of return; fixed income securities, yield, duration and immunization; portfolio optimization, mean-variance models, Capital Asset Pricing Model and Arbitrage Pricing Theory; forwards, futures, swaps and risk hedging; pricing derivative securities and options, binomial market models, continuous market models and Black-Scholes equation.
Credits: 3

INDR 574 / STOCHASTIC MODELS IN FINANCIAL ENGINEERING

Review of basic stochastic concepts; binomial market models and pricing of derivative securities; Wiener process and Brownian motion; martingales; stochastic integrals and differential equations; Its calculus; pricing of derivative securities in continuous markets; Black-Scholes model; foreign exchange, bond and interest rate markets.
Credits: 3
Prerequisite: INDR. 503 or consent of the instructor

INDR 576 / INVENTORY CONTROL THEORY

Development and application of mathematical models for inventory management. Basic economic-order-quantity with extensions; time-varying demand and purchase costs. Multiechelon inventory systems with multiple products and/or multiple locations. Analysis of stochastic demand for single and multiple products. Analysis of stochastic lead times. Policy optimization under time-varying, stochastic demand.
Credits: 3
Prerequisite: INDR. 503 or consent of the instructor

INDR 578 / ADVANCED MODELS IN SUPPLY CHAIN MANAGEMENT

Dynamic inventory policies for single-stage inventory systems: concepts of optimality and optimal policies. Multi-Echelon Systems: uncapacitated models and optimal policies, capacitated models: different control mechanisms. Multiple locations and multiple items: inventory and capacity allocation. Decentralized control and the effects of competition on the supply chain: coordination and contracting issues.
Credits: 3
Prerequisite: (INDR. 503 ) or consent of the instructor

INDR 583 / SUPPLY CHAIN MODELING AND ANALYSIS

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.
Credits: 3

INDR 584 / LOGISTICS MANAGEMENT

Formulation of integer and combinatorial optimization problems Introduction to logistics systems; logistics network design, location models; warehouse design, tactical decisions, operational decisions; transportation management; planning and managing freight transportation; fleet management, vehicle routing problem.
Credits: 3
Prerequisite: INDR. 262 or consent of the instructor

INDR 586 / HEALTH AND HUMANITARIAN LOGISTICS

Principles of logistics and supply chain operations in the humanitarian context and health care systems. Broad understanding of how Operations Research techniques can be used in humanitarian operations and response functions by case studies. Mathematical modeling and solution of decision making problems in disaster mitigation, response and recovery operations that involve planning and design functions. Logistic problems arising in the healthcare sector such as ambulance assignment and routing in medical emergency response, blood collection and inventory management. Location of public service facilities such as hospitals and fire stations for long-term development.
Credits: 3