Industrial Engineering

INDE 501 - FUNDAMENTALS OF INDUSTRIAL ENGINEERING

Short Title: FUND INDUSTRIAL ENGINEERING

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Description: Introduction to fundamental tools in industrial engineering. Topics include productivity analysis, material handling, logistics, design of experiments, quality control, location theory, warehouse design, supply chain management and scheduling.

INDE 509 - INTRODUCTION TO HUMAN FACTORS ENGINEERING

Short Title: INTRO TO HUMAN FACTORS ENG

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): INDE 501

Description: Analysis and design of engineering systems considering human characteristics and limitations. Design of control, displays, tools, workstations and groups. Human factors research methods. Instructor Permission Required.

INDE 511 - GRAPH ALGORITHMS

Short Title: GRAPH ALGORITHMS

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: Graph Algorithms in Operations Research. Topics include: spanning trees, graph search algorithms, shortest path problems, worst case time complexity analysis, computational complexity, dominating set problems, vertex and edge cover problems, python implementations, and other problems in graph optimization. Instructor Permission Required. Graduate/Undergraduate Equivalency: CMOR 446. Recommended Prerequisite(s): INDE 545 or CAAM 378 Mutually Exclusive: Cannot register for INDE 511 if student has credit for CMOR 446.

INDE 513 - OPERATIONS RESEARCH IN HEALTHCARE

Short Title: OPER RES IN HEALTHCARE

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Description: Operations research in healthcare systems and medical decision-making. Application areas will include hospital resource management, patient scheduling, treatment planning and organ transplantation. Modeling techniques will include mathematical programming, stochastic processes, Markov decision processes and simulation. Graduate/Undergraduate Equivalency: CMOR 463. Recommended Prerequisite(s): INDE 545 and INDE 572 Mutually Exclusive: Cannot register for INDE 513 if student has credit for CMOR 463.

INDE 517 - OPTIMIZATION FOUNDATIONS OF DATA SCIENCE

Short Title: OPT FOUNDTNS OF DATA SCIENCE

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Description: Optimization methods for machine learning. Topics included are as follows: basics of optimization theory, gradient-based optimization (e.g., gradient descent, stochastic gradient descents, AdaGrad, Adam, RMSProp, etc.), linear regression and its extensions (e.g., ridge regression and lasso), least-squares classification and logistic regression, Newton methods in machine learning, basics of constrained optimization, Lagrangian relaxation and duality, support vector machines, and optimization in neural networks.

INDE 543 - MANUFACTURING PROCESSES AND SYSTEMS

Short Title: MANUFACTURING PROC AND SYS

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Prerequisite(s): INDE 501

Description: Fundamentals of manufacturing processes and systems. Topics include machining, casting, 2D printing, material flow, capacities, bottlenecks, and just-in-time systems. Simulation and optimization of various manufacturing systems. Trade-offs among various processes. Instructor Permission Required.

INDE 545 - PRESCRIPTIVE ANALYTICS

Short Title: PRESCRIPTIVE ANALYTICS

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Description: A survey of methods for combining mathematical models and large data sets to produce optimal decisions. Topics include decision analysis, dynamic programs, mathematical programs and various heuristics. Instructor Permission Required.

INDE 546 - COMPUTATIONAL PRESCRIPTIVE ANALYTICS

Short Title: COMP PRESCRIPTIVE ANAYLTICS

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Prerequisite(s): INDE 545

Description: A continuation of INDE 545 that focuses on computational approaches to prescriptive analytics. Topics include decomposition approaches to large-scale optimization, modeling languages, decision analysis and discrete-even simulation software. Emphasis will be placed on using relevant software on practical problems. Graduate/Undergraduate Equivalency: CMOR 442. Mutually Exclusive: Cannot register for INDE 546 if student has credit for CAAM 476.

INDE 561 - SUPPLY CHAIN MANAGEMENT

Short Title: SUPPLY CHAIN MANAGEMENT

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): INDE 545

Description: Supply chain management is the integrated management of the flow of materials, products, services, and cash from the suppliers all the way to the customers and from the customers back to the suppliers. Due to the complex nature of today’s supply chains, effective management of these flows is a challenging task. This course aims to familiarize students with the concepts and models that are useful in designing and managing effective and efficient supply chains. Topics include facility location and distribution models, forecasting, sales & operations planning, supply chain coordination, inventory management, transportation, supplier selection, pricing & revenue management, and sustainability in supply chains. Graduate/Undergraduate Equivalency: CMOR 461. Mutually Exclusive: Cannot register for INDE 561 if student has credit for CAAM 421.

INDE 562 - INTRODUCTION TO CONTINUOUS OPTIMIZATION

Short Title: INTRO TO CONTINUOUS OPT

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: An introduction to the formulation of unconstrained and constrained optimization models, and their numerical implementations to problems in science and engineering. Emphasis on Newton-type and interior-point methodologies. Instructor Permission Required. Recommended Prerequisite(s): INDE 545 or CAAM 378

INDE 565 - REVENUE MANAGEMENT & PRICING

Short Title: REVENUE MGMT & PRICING

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: Ever wondered why airfares change? Why a person in the seat next to yours got a better deal or perhaps why you were able to fly to your desired destination for pennies? And, why airlines often ask whether you are willing to take another flight in exchange for a voucher? The above are just one sort of examples where prices for travel services seem to change for reasons that are not immediately clear. People have been intrigued about these practices and with the advent of the already ubiquitous e-commerce prices that change dynamically are part of everyday life. Revenue management ('RM') and dynamic pricing are the science engines that power that dynamic. In this course we will study both theory and practice of revenue management and dynamic pricing with a focus on its application areas. The goal is to find a scientifically sound answer to the question of what product a firm should offer to a customer at any given time, given capacity constraints and, possibly, some other information. This economics problem has often been paraphrased as ‘selling the right product to the right customer at the right price’ and has become acute in thin-margin industries such as airlines (after deregulation in the 70’s), where even a small improvement in revenue implies a major improvement to profit. Several early adopters in other industries having similar features followed shortly, e.g., hotels, cruise lines, and car rental companies. The wealth of information and the ease of modifying customer offering that came with the advent of the Internet spurred an incredible expansion in the number of industries adopting the discipline of RM and gave rise to several closely related areas of new techniques and applications such as dynamic pricing, offer optimization, multi-sided markets (e.g. ride-sharing platforms, online auctions) and other aspects of e-commerce. In the course we will start with the basic RM concepts and an overview of the most popular fields of applications. Then we will explore in depth the science behind most popular techniques used there. Therefore, at least some level of quantitative background will be required (OR, STEM). We will cover main features of state-of-the-art revenue optimization algorithms, as well as forecasting and estimation of demand models that are applicable to real-life data. Forecasting and optimization are two crucial buildings blocks in typical RM or pricing applications, and we will show how these can be enriched with more recent data-driven and machine learning techniques. Graduate/Undergraduate Equivalency: CMOR 465. Mutually Exclusive: Cannot register for INDE 565 if student has credit for CMOR 465.

INDE 567 - OPTIMIZATION METHODS IN FINANCE

Short Title: OPT METHODS IN FINANCE

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): MATH 212 and (CAAM 210 or CMOR 220)

Description: Fundamentals of financial optimization. Asset-liability management, arbitrage and asset pricing, mean-variance models, portfolio optimization. This course covers models and algorithms for solving linear, quadratic, integer, and stochastic optimization models encountered in financial and data science applications. Students who have taken CAAM 467 should consult their advisor before attempting to register for INDE 567. Department Permission Required. Graduate/Undergraduate Equivalency: CMOR 462. Recommended Prerequisite(s): INDE 545 Mutually Exclusive: Cannot register for INDE 567 if student has credit for CAAM 467.

INDE 571 - PROBABILITY AND STATISTICAL INFERENCE

Short Title: PROB & STATISTICAL INFERENCE

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Description: Topics include probability, random variables, probability distributions, transformations, moment generating functions, common families of distributions, independence, sampling and convergence, basics of estimation theory, hypothesis testing, Bayesian inference, ANOVA, regression. Introduction to statistical software. Department Permission Required.

INDE 572 - STOCHASTIC PROCESSES AND SIMULATION

Short Title: STOCH PROCESSES & SIMULATION

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): INDE 571

Description: Topics include Markov chains, renewal processes, queueing theory, statistical quality control, discrete-event simulation, random number generators, Monte Carlo methods, resampling methods, Markov Chain Monte Carlo, importance sampling and simulation based estimation for stochastic processes.

INDE 573 - DISCRETE-EVENT SIMULATION

Short Title: DISCRETE-EVENT SIMULATION

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Prerequisite(s): (STAT 518 and STAT 519) or INDE 571

Description: Simulation of discrete-event dynamic systems. Topics include introduction to simulation models; modeling with Simio, a comprehensive simulation package with animation capabilities; statistical aspects such as input and output analysis, random variate generation, variance reduction techniques; optimization via simulation. Students who have taken CAAM 485 should consult their advisor before attempting to register for INDE 573. Department Permission Required.

INDE 577 - DATA SCIENCE AND MACHINE LEARNING

Short Title: DATA SCI & MACHINE LEARNING

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students. Enrollment limited to students in a Master of Comp & Appl Math, Master of Comp Sci & Eng, Master of Computer Science, Master of Data Science, Master of Electrical Comp Eng, Master of Eng Mgmt & Leadershp, Master of Industrial Eng, Master of Mechanical Eng or Master of Statistics degrees.

Course Level: Graduate

Description: Fundamentals of data science and machine learning. Topics include: introduction to scikit-learn, Keras and tensorflow2, linear and logistic regression, clustering, support vector machines, random forest trees, neural networks, deep learning, natural language processing. Graduate/Undergraduate Equivalency: CMOR 438. Recommended Prerequisite(s): Three semesters of calculus recommended. A background in some programming language would be extremely useful. Mutually Exclusive: Cannot register for INDE 577 if student has credit for CMOR 438.

INDE 578 - DEEP AND REINFORCEMENT LEARNING

Short Title: DEEP AND REINFORCEMENT LEARNIN

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: This graduate-level course offers an in-depth exploration of Deep Learning (DL) and Reinforcement Learning (RL). The course is structured to provide a comprehensive understanding of both DL and RL, ensuring students are well-equipped to tackle complex problems in these fields.

INDE 590 - MASTER'S IN INDUSTRIAL ENGINEERING CAPSTONE EXPERIENCE

Short Title: MIE CAPSTONE EXPERIENCE

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Research

Credit Hour: 1

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: MIE students are required to write a field report related to one of the required core courses in the curriculum. Students should coordinate this with the INDE 590 instructor/capstone director, prepare a report relevant to the course material, and present it in class. Instructor Permission Required. Recommended Prerequisite(s): INDE 501 and INDE 545 and INDE 571. Repeatable for Credit.

INDE 597 - TOPICS IN INDUSTRIAL ENGINEERNG

Short Title: TOPICS IN INDUSTRIAL ENG

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Lecture

Credit Hours: 3

Restrictions: Enrollment is limited to Graduate level students.

Course Level: Graduate

Description: Topics and credit hours vary each semester. Contact department for current semester's topic(s). Instructor Permission Required. Repeatable for Credit.

INDE 677 - SPECIAL TOPICS

Short Title: SPECIAL TOPICS

Department: Industrial Engineering

Grade Mode: Standard Letter

Course Type: Internship/Practicum, Laboratory, Lecture, Seminar, Independent Study

Credit Hours: 1-4

Restrictions: Enrollment is limited to Graduate or Visiting Graduate level students.

Course Level: Graduate

Description: Topics and credit hours vary each semester. Contact department for current semester's topic(s). Repeatable for Credit.