Course Descriptions

Offered: Spring & Fall
Engineering Project Management is a comprehensive course designed to provide engineering students with a robust understanding of project management frameworks and their practical applications for managing engineering projects. It covers the philosophy, methodologies, and processes critical to managing engineering projects from initiation to completion. Students will delve into the roles and responsibilities of project managers, develop skills in key areas such as scope, schedule, cost, and quality management, and gain expertise in resource, communication, and risk management. Through a combination of lectures and case studies, in-class activities, and a final group project, participants will practice applying these concepts to real-world engineering projects. The course also emphasizes effective stakeholder engagement and the use of tools and documentation to ensure project success. Suitable for aspiring technical project managers and professionals across industries, this course equips students with the knowledge and skills to navigate the complexities of modern engineering project management." This course requires prior technical understanding and engineering background, since the course is designed for engineering project management.

Offered: Fall
Data analytics is the use of computational statistics and data mining to draw insights and build predictive models based on large data sets. As data becomes more prevalent across many different areas of importance in engineering, policy analysis, and management, analytics is becoming an increasingly important topic. This course assumes a working knowledge of regression and statistics and builds from this to introduce modern data analytics. The course covers fundamental concepts of predictive modeling and major classes of methods beyond linear regression, including additive models, tree-based models, multi-level models, boosting, bagging, and model averaging. The course focuses on the application and interpretation of the methods while also providing an understanding of the underlying basis and theory behind them. Homework assignments, the midterm, and the term project are primarily data-driven analytics exercises. Pop-up quizzes will be used to test the basic concepts and theories. Opportunities will be given to students to work on projects of their own interest, provided they are relevant and aligned with the learning outcomes of the course.

Offered: Fall
This course provides an introduction to the fundamentals of probability and distributions, several classical and state-of-the-art machine learning methods and their applications for engineers. Fundamentals of linear model and shallow neural networks, multilayer perceptrons, and deep neural networks will be covered. Modern convolutional neural networks, recurrent neural networks, and optimization techniques will be discussed with engineering examples implemented in Python. After taking this course, students should be able to do the following: (1) Understand the basics of machine learning, including classical and advanced machine learning models; (2) Understand modern convolutional neural networks and recurrent neural networks; (3) Understand attention mechanism in neural networks; (4) Understand basic loss functions and optimization methods; (5) Implement classical and advanced machine learning models in Python; (6)  Select, compare, and use an appropriate machine learning models in specific engineering applications; (7)  Create new model structures, implement in Python, and validate on data sets.

Offered: Fall
This course provides students with knowledge and experience in solving real-life problems related to human performance and safety in the workplace. It requires students to complete a series of readings that prepare them for understanding and solving problems in human factors, ergonomics and safety in the workplace. Students learn about the importance of ergonomics to improve occupational systems, how to identify problems and complete the appropriate systematic job analyses to characterize problems, and how to solve and implement ergonomics solutions.

Offered: Spring
The use of artificial intelligence (AI) to solve real-world problems is ubiquitous, e.g., AlphaFold by Google DeepMinds to predict protein structure and ChatGPT by OpenAI, a generative AI chatbot for interactive text-based support. Therefore, to be an active participant in this new AI-augmented future, the next generation of engineers must also equip themselves with the knowledge of AI techniques. To this end, the “Introduction to Deep Learning for Engineers” course will introduce a pathway to modern AI called deep learning. On a high level, deep learning extends the traditional machine learning approaches by avoiding the need to hand-craft features. This course would be ideal for students with domain knowledge willing to learn and apply deep learning tools in their respective fields. The main topics covered in this course include Deep Feedforward Networks, Regularization, Training Deep Networks, Convolutional Networks, Sequence Modeling, Practical Methodologies, and Applications of Deep Learning Systems to Engineering Problems. Advanced topics like Autoencoders and Deep Generative Models will be covered if time permits. Additionally, most topics would accompany in-class demonstrations in PyTorch, an open-source machine-learning framework, and coding assignments in Python using NumPy, a scientific computing package.

Offered: Spring
This course delves into the rapidly expanding world of non-intrusive sensors as they apply to human-centered computing. With a mix of theoretical and practical content, you will comprehensively understand quantitative and qualitative data analytics approaches in human-centric data. Additionally, the course will explore the application of computer vision and machine learning to estimate human states and behaviors using human-centric data.

Offered: Spring
Product Lifecycle Management (PLM) is a cornerstone for organizations aiming to bring innovative products to market quickly, efficiently, and sustainably. This course offers a deep dive into PLM’s role as a strategic, data-centric framework that empowers collaboration across teams, streamlines processes, and enhances decision-making throughout a product’s entire lifecycle—from ideation through commercialization. Through team-based projects, real-world case studies, and hands-on experience with Autodesk Fusion Manage (a leading cloud-based PLM solution), students will learn how to effectively manage and drive product success in today’s competitive landscape.

Offered: Spring & Fall 
Individual problems/research on topics as determined by students working individually with a faculty member. If the faculty member agrees to supervise the work, the student should register for IE 501 (Fall) or IE 502 (Spring) as an M.S. student, or for IE 601 (Fall) or IE 602 (Spring) as a PhD student.  These courses have variable credit offerings, and require the written consent of the instructor. Do not sign up for such a course without first talking to the instructor. A maximum of six credit hours of informal course work may be applied toward the minimum 30 credit-hour requirement for the master's degree.

Offered: Fall
This course teaches the analytical tools necessary to effectively tackle the problem of designing the layout and location of facilities. Both non-quantitative and quantitative, and computer-based approaches are used in this course. The location problems covered include analytical methods to determine optimal facility location, locations of machines/ workstations/work areas in a manufacturing facility. The course considers specialized facility design applications, such as, service-oriented facility layouts, warehouse storage policies, and post-disaster facility planning.  Topics include most recent facility management technological trends, i.e., WMS, RFID, Autonomous vehicles (UAVs, UGVs), and IoT. Material from recent research papers will be used to supplement the course material.

Offered: Spring
This course covers the production management related problems in manufacturing systems. It blends quantitative and qualitative material, theoretical and practical perspectives, and thus, bears relevance for academic as well as industrial pursuits. The introduction consists of the production and operations management strategy. The topics covered include simple forecasting methods, workforce planning, inventory control, production planning, materials requirements planning, operations scheduling, and project management. Recent developments in production management such as just-in-time (JIT) inventory systems, and flexible manufacturing systems (FMS) are also discussed.

Offered: Fall
This course is concerned with the basic and important principles in computer-integrated manufacturing (CIM). Based on an understanding of modern production and manufacturing systems, the course will further introduce the use of computers for the integration of all functional areas in a manufacturing enterprise. Topics include: computer-aided design (CAD), geometric modeling and data structures, computer-aided manufacturing (CAM), computer-aided process planning (CAPP), robotics, automation, and additive manufacturing (AM). Labratory assignments are included.
 

Offered: Spring
This course aims to equip engineering students with the statistical foundation and practical skills necessary to effectively plan, design, and analyze experiments in an engineering context. Upon successful completion of the course, the students are expected to: (1) Identify and apply appropriate experimental designs to address specific engineering research or process-improvement questions. (2) Employ statistical analysis techniques to interpret experimental data, quantify variability, and draw evidence-based conclusions. (3) Optimize product and process performance by systematically evaluating critical design parameters and their interactions. (4) Use relevant software tools to streamline data collection, analysis, and reporting of experimental outcomes. (5) Communicate results effectively to both technical and non-technical stakeholders, facilitating data-driven decision-making in engineering environments.

Offered: Spring & Fall
Familiarizes students with the application of statistical quality problem-solving methodologies used to characterize, leverage, and reduce process variability. This course emphasizes the application of sampling methodologies, sample size determination, hypothesis testing, analysis of variance, correlation, regression, measurement systems analysis, design and analysis of saturated experimental designs, design and analysis response surface experimental designs, and statistical process control.

Offered: Fall
This course provides a comprehensive, applied introduction to Six Sigma methodologies for quality and process improvement, with a strong emphasis on data-driven decision-making and real-world problem-solving. Students learn to identify and execute high-impact, customer-focused quality improvement projects using structured Six Sigma methodologies, including DMAIC (Define, Measure, Analyze, Improve, Control). Topics include but are not limited to: six sigma metrics, quality tools and problem-solving methods, measurement system analysis and process capability, design of experiments and root cause analysis, leadership and change management. The focus of this course is centered on practical application. By the end of the course, students will be able to lead and contribute to Six Sigma projects, interpret statistical analysis results, and drive data-based process improvements.

Offered: Spring
This breadth-focused course reviews concepts, models, and consequences of social network formation and behavior. It will rely on scholarship on the science of networks in communication, computer science, economics, engineering, organizational science, life sciences, physical sciences, political science, and sociology, with the purpose of covering theories, methods, and software tools to examine the structure and dynamics of networks.

Offered: Based on Demand
This course provides an overview of modeling techniques and methods used in decision making with uncertainty, including multi-attribute utility models, influence diagrams, decision trees, and Bayesian models. Psychological components of decision making are discussed. Elicitation techniques for model building are emphasized. Practical applications through real world model building are described and conducted, including business management, supply chain and logistics, transportation, health care, architectural design, and homeland security. Each student will work on a separate project throughout the semester, including presentations and written reports. 

Offered: Based on Demand
This course aims to provide students with a general background of various statistical analysis techniques and data mining methods that are used in transportation systems. It covers various practical analytical topics in transportation and logistics, including model estimation, data analysis, traffic forecasting, and incident prediction. A broad range of transportation related techniques are covered in statistics and data analysis skills, such as Logistic Regression, and Time Series Modeling. Popular statistical modeling software will be used to solve various practical problems.  

Offered: Spring
This course discusses the principles of green manufacturing including (1) lower usage of materials and energy (2) substitution of non-renewable with renewable input materials (3) reduce unwanted outputs/waste (4) close the loop (convert outputs to inputs through recycling, recovery, reuse) (5) re-engineering the structure of the systems through revised supply chain structure and changing the ownership concept in the system (introduction of product service systems).

Offered: Fall
This course is a broad introduction to the field of human factors and ergonomics, focusing on the study of the interaction of humans with tasks, equipment and computers, and the environment in the workplace system. We will study human capabilities and limitations in order to understand how to better design systems which include humans so that the systems can be efficient, effective, and error-free. This course will contain principles and methods which address human cognitive and perceptual systems, as well as physical aspects of humans. The course serves as an introduction for those who wish to pursue further graduate level work in Human Factors, or for those needing a broad background in Industrial Engineering.

Offered: Spring
The purpose of this course is to allow students to gain familiarity with a broad range of methods appropriate for studying humans, tasks, environments, and their interaction; to be able to formulate research hypotheses, and to understand the relationship between research hypotheses and appropriate methods for testing the hypotheses. Students will read journal papers demonstrating a variety of research methods, as well as learn how to prepare a research proposal.

Offered: Every other Fall
Introduction to basic behavioral and psychological factors, such as sensory, perceptual, learning, and cognitive processes. Emphasis is placed upon the application of knowledge about these factors to the design and development of human-machine systems.

Offered: Based on Demand
This course introduces graduate and senior-level undergraduate students to the principles and methods of human-computer interaction (HCI) and the design of effective interactive systems. Rather than focusing on technological constraints and capabilities, HCI emphasizes designing systems that support user needs, capabilities, and task requirements. This course will provide students the opportunity to gain in-depth knowledge in the area of human factors, as well as the opportunity to apply principles of user- and use-centered design to a real world design problem.

Offered: Spring
Introduction to the structure and functioning of the human body, including anthropometry, biomechanics, and physiology. Predictive models of human interaction with task factors such as posture and workload, and environmental factors such as temperature and humidity. Emphasis is on the applications and implications of physiological measures such as energy expenditures, heart rate, and E.M.G. IE 538 Human Factors Laboratory This course provides techniques for testing hypotheses and making numerical estimates based on data collected on human subjects. The lecturer content covers measurement strategies, issues of simulation fidelity, and laboratory vs. field experimentation. The laboratory and field content provides a series of tests of current issues in human factors practice from manufacturing, transportation, and office systems. 

Offered: Based on Demand
This course provides techniques for testing hypotheses and making numerical estimates based on data collected on human subjects. The lecture content covers measurement strategies, issues of simulation fidelity, and laboratory vs. field experimentation. The laboratory content provides a series of tests of current issues in human factors and ergonomics practice from manufacturing, transportation, and healthcare. Topics will include assessing injury risk, balance and posture control, human motion analysis, muscle activity, fatigue, ergonomics for special populations such as the aging and obese, and the combined effects of mental and physical demands. Readings will be selected to put the use of various instruments and measurement systems into an ergonomics perspective. During the course of this class, we will examine the basis of data collection and analysis, and perform a series of small, complete studies designed to demonstrate different data collection/analysis techniques.

Offered: Spring
This course covers the complexities of accident causation, the differences between accident, incident, hazard and risk, how systems safety is applied in the context of the system life cycle, modeling accidents with chain of events and hierarchical approaches, including the NTSB and NSC models, how to systematically identify potential hazards in simple and complex systems using techniques such as “what if”, “five whys”, FMEA, FMECA and fault-tree hazard analyses techniques, how to apply hazard analyses to the evaluation and risk reduction work environments and consumer products, basic principles of product safety, warnings and instructions, the components of hazard communication and record keeping OS&H standards.

Offered: Fall
This course provides an overview of concepts of operations research methodology. This course introduces linear and integer programming emphasizing transportation and logistics applications and an optimization software tool such as CPLEX. This is an introductory course for operations research concepts. IE students that are not specializing in OR and non-IE students interested in operations research are encouraged to enroll. This course is the same as STL 502, and course repeat rules will apply.  Students should consult with their major department regarding any restrictions on their degree requirements.

Offered: Spring
This introductory course on computer simulation covers spreadsheet simulation, discrete event simulation, system dynamics simulation and agent-based simulation with the focus on key statistical analysis of data and practice-oriented theory. Topics include generating random numbers and varieties, selecting input probability distribution, hypothesis testing for the statistical and practical significance of simulation through lab assignments, and test their gained skills in team projects inspired by real world simulation applications. 

Offered: Based on Demand
This course provides an introduction to the methods and issues involved in the design and development of real-world multi-sensor information fusion systems, and during the course there will be overviews of existing real systems, to include possible field trips. The course will review the taxonomy of functional architectures, architectural design methods and standards, requirements derivation, system modeling and performance evaluation. Students should come away from this course with an understanding of, and some limited experience with, the methods and mind-set for the comprehensive design, development, and test of Information Fusion systems.

Offered: Based on Demand
This course will introduce High Level Information Fusion concepts and methods to give students a good understanding of this new area of research that is the combination of multidisciplinary classical fields of research. This course will give a brief introduction of a number of Fusion Models and how its different subcomponents interact with each other. The course will focus on Information Fusion as it relates to Situational Awareness/Understanding, Impact/Threat Assessment and Process Refinement (which is called High Level Fusion in one of the models we will be studied).

Offered: Fall
This course focuses on the development of the fundamental programming skills required by today’s operations research (OR) professionals. In particular, Python will be the programming language employed by this course. The course will begin with an overview of general Python syntax and usage, and will then focus on the use of Python to solve a variety of classic/common problems encountered in the operations research and production systems domains.

Independent research leading to MS Thesis. A student must be recommended to the DGS by a faculty member to complete a thesis. The faculty member determines the credit amount and expectations for a successful thesis. Thesis students register for IE 559/560 and may take a maximum of 6 credits to earn their degree.

Offered: Spring
This course delves into the core principles of Lean thinking, a powerful philosophy focused on eliminating waste and maximizing value. We'll explore how these seemingly simple concepts, such as value stream mapping, flow, pull, and continuous improvement (Kaizen), drive profound transformations in production systems. Moving beyond traditional manufacturing, we'll examine the practical application of Lean methodologies in diverse workplaces, including service industries and administrative functions. Students will learn to identify and eliminate waste, streamline processes, and foster a culture of continuous improvement, leading to enhanced efficiency, customer satisfaction, and overall organizational performance.

Offered: Fall
This course will be an intensive study of Linear Programming (LP).  LP deals with the problem of minimizing or maximizing a linear function in the presence of linear equality and/or inequality constraints. Both the general theory and characteristics of LP optimization problems as well as effective solution algorithms and applications will be addressed. The course is a good one for students who are planning to apply Operations Research (OR) tools in all areas of application in the public and private sectors including production or manufacturing problems and service/logistics related problems as well as to learn an optimization software tool called OPL/CPLEX. This course is part of the core for the MS and PhD degrees concentrating in OR; therefore comprehension of the underlying mathematical theory/why things work is emphasized.

Offered: Spring
Basic theory of Discrete Optimization as well as the computational strategies for exact and heuristic solution of problems having discrete decision variables. Discrete Models can be divided into two main categories: Integer Programming and Combinatorial Optimization. Integer programming encompasses models with a mixture of discrete and continuous decision variables, and ones for which efficient algorithms are not likely to be found. On the other hand combinatorial models may deal with problems having pure discrete elements for which clean and efficient procedures exist. This latest class includes Network Optimization. This course will place emphasis on Integer Programming and related areas. The course is a good one for students who are planning to apply OR tools in Production or Manufacturing problems or supply chain/service/logistics related problems as well as continue using an optimization software tool called CPLEX or Gurobi. 

Offered: Fall
This course teaches the fundamentals of applied probability theory. Topics include algebra of events; sample space representation of the model of an experiment (any non-deterministic process); random variables; derived probability distributions; discrete and continuous transforms and random incidence. The course also introduces elementary stochastic processes including Bernoulli and Poisson processes and general discrete-state Markov processes. This is followed by a discussion of some basic limit theorems and some common issues and techniques of both classical and Bayesian statistics.

Offered: Spring
A continuation of IE 575. Topics include discrete-time and continuous-time Markov chains, queuing theory, Bayesian statistical inference and classical statistics. 

Offered: Spring & Fall
The application of breakthrough information technologies has enabled companies to look at their supply chains as a revolutionary source of competitive advantage. This course includes two major parts. In the first part, information technology foundation for e-business and supply chain will be discussed. This part will be concluded by a comprehensive framework for the smart supply chain management. The framework shows how emerging technology is being employed in a supply chain to optimize supply chain operations. The second section of the course emphasizes on the supply chain design using engineering methods such as analytics and operations research. Supply chain design methods include aggregate planning, transportation, supplier selection and demand forecasting. Several case studies will be discussed.

Offered: Spring
This course introduces Industrial Engineering students to robots and robotic systems, including the design of robot controllers, coordination of multiple robots, simulation of robotic systems, and optimization of robot task scheduling.

Offered: Spring
This course introduces business intelligence and analytics, defined as the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions. The development and use of data warehouses and data marts, and the application of selected data (including text and web) mining techniques to business decision making is illustrated. Students actively participate in the delivery of the course through case and project presentations.

Offered: Fall
This practical course will discuss a variety of topics about the healthcare industry and the industrial engineers’ roles. Classes have lectures and invited guest speakers, including healthcare professionals and industrial engineers (previous graduates) serving as systems analysts. Course work include assignments and a group project in healthcare engineering, solving real problems related healthcare systems, such as improvement of care quality, lean six sigma in healthcare, challenges in operations, issues in health IT implementation, and hospital financial performance.

In this course, students engage in a hands-on capstone project with faculty, industry, and other partners that centers on the application of specific engineering principles and methodologies in real-world settings. This course is required for the project track MS international students participating in CPT, or ME students. The MS students register for IE 591; the ME students register for IE 592. While on a CPT, students earn at least 1, and up to 3, IE 591 or IE 592 credits.

Offered: Based on Demand
The purpose of this course is to allow students to gain familiarity with human factors research methods and knowledge associated with driver safety. Students will be able to formulate course-relevant research hypotheses, design studies, and gain experiences with data collection and analysis associated with common research methods in the traffic safety domain.

Individual problems/research on topics as determined by students working individually with a faculty member. See department and/or advisor for additional information.

This is a special topics course in which the content changes annually.  Recent offerings of this course have focused applied work measurement methods, applications of ergonomics for vulnerable populations and work-related musculoskeletal epidemiology.

Offered: Based on Demand
The purpose of the course is to explore theoretical and methodological issues in the field of cognitive engineering - a field which relates knowledge in diverse fields such as psychology, artificial intelligence, sociology, and human factors engineering to the design of complex human-machine systems. Course topics include issues in computerization and work, knowledge representation, problem solving, mental models, situated action, applications of ecological psychology, cognitive artifacts, distributed cognition, decision making, and methods in cognitive engineering.  

Offered: Based on Demand
This is a special topics course in which the content changes annually.  Recent offerings of this course have included occupational field research methods in human factors and ergonomics, innovations in home health and healthcare, and innovations in inclusive autonomous vehicle design.

Independent research leading to PhD dissertation.

Offered: Fall
In-depth analysis of selected topics in Operations Research. Course content will focus upon particular interests of the students and the instructor.

Offered: Spring
This course seeks to familiarize students with problems that arise from the design, operation, and optimization of logistics systems. We will give special attention to operational research and mathematical modeling techniques that are commonly used for solving these problems. By the end of the course, it is expected that students will develop an in-depth understanding of the solution techniques covered in class to the point of being able to apply and extend those techniques for solving similar problems in a research-level context. This course is primarily intended for Ph.D. students; nonetheless, as a technical elective, it may be suitable for Master's students with an appropriate background in operations research.

Offered: Spring
Data science is an interdisciplinary approach to collecting, pre-processing, and analyzing data from various systems to help in informed decision-making. Data-driven risk analytics is based on principles of data science that would help to identify and assess the risks of a system by collecting data on measurable goals / KPIs, analyzing historical patterns, and gathering insights from the past to predict systemic risks in the future. In this course, we will discuss various research papers leveraging state-of-the-art advanced data-driven techniques to analyze the risks of various systems, contributing to risk-informed decision-making. Application areas will mostly include topics from data-informed risk analytics in disaster management, health, and energy systems. This course assumes a working knowledge of the fundamentals of statistical learning, predictive modeling, and programming in R. The course will include discussions on concepts and applications of Bayesian models, multivariate tree-boosting models, neural networks, time series analysis, and other ensemble models in the context of risk and decision analytics. Research paper discussions, class presentations, midterms, pop-up in-class quizzes, and a term project will be the primary assignments for this course. The students will have to work on projects in one of the related areas pertaining to their interests, provided they are relevant and aligned with the learning outcomes of the course.

Offered: Based on Demand

Offered: Based on Demand

Offered: Fall
The course will introduce the fundamentals of nonlinear optimization, geared towards doctoral and advanced graduate students. The emphasis is on the theoretical foundations of optimization, classical mathematical programming, as well as models and solution techniques. Although some applications and algorithms are discussed, the emphasis is theoretical. The focus of the course will be on convex analysis (convex sets, separation theorems, convex functions), optimality conditions (Fritz-John & Karush-Kuhn-Tucker), Lagrangian duality, and iterative solution methods. This course is primarily intended for doctoral students; nonetheless, as a technical elective, it may be suitable for advanced master’s students with an appropriate background in operations research. At the end of the course, students will be able to (1) mathematically prove fundamental results in optimization, (2) recognize and formulate nonlinear optimization problems, (3) identify when a problem is convex, (4) understand classical results characterizing optimal solutions to these problems, (5) use these classical results in application problems and design of algorithms, and (6) use optimization software (e.g., Pyomo) to implement optimization models.

Offered: Based on Demand
This course will start with the fundamentals of individual and group decision analysis, introduce both sequential and simultaneous-move models, for both games of complete and incomplete information. This course will then introduce advanced topics such as mechanism design, signaling, screening, repeated games, behavioral games and evolutionary games. Finally, this course will introduce some state-of-the-art game-theoretic research on supply chain management, transportation, health care, architectural design, and homeland security. Each student will work on a separate project throughout the semester, including presentations and written reports. 

Offered: Based on Demand
This course will give a high level description of Graph and corresponding Algorithms. The instructor will attempt to give the basic theory as well as the computational strategies for exact heuristic solution of graph based problems.

Offered: Based on Demand
This is an applied Operations Research course, where the focus is on the utilization of the analytical tools that students have learned in other Operations Research courses to study problems of urban significance. The course starts off with a review of basic probabilistic concepts. The first topic covered is that of geometrical probability, a powerful tool to approach urban problems. Then a discussion on queuing theory is presented. This is followed by a discussion of spatial queues that are used in modeling urban emergency service systems. The next topic is on network problems that are useful in an urban context. The final topic is on simulation modeling as applied to urban problems. All topics are reinforced with real-world examples and in-depth homework assignments.

Offered: Based on Demand
Both from a theoretical and practical perspective, Multiple Criteria Decision Making (MCDM) influences all aspects of engineering design, analysis and decision making. The goal of MCDM is to help a human decision maker (DM) consider several conflicting objectives simultaneously to find one or more Pareto optimal solutions that satisfy a DM's preferences. Trade-offs must be considered since no single solution individually optimizes each criterion. Theory and application will be studied. Methods can be classified as (1) No-preference methods (2) a Priori methods (DM preference information before considering alternatives (3) A posteriori methods (DM preference information after generating alternatives) and (4) Interactive methods (solution algorithms formed with DM preference information and repeated with new information at each iteration).

Offered: Based on Demand
This is an advanced graduate level course which will cover in-depth concepts of additive manufacturing (3D printing) technology. The course aims to help graduate students in understanding the latest development and critical challenges of 3D printing and provide students with related techniques and practical experience in developing novel 3D printing process and applications. The topics include rapid prototyping and tooling techniques, rapid manufacturing and its impact on society, process improvement techniques, material issues. post processing for additive manufacturing, geometry creation and handling.

Offered: Spring & Fall
This course brings together leading scholars, researchers, and experts to present and discuss cutting-edge research from multiple Industrial Engineering disciplines. This course aims to inspire and stimulate intellectual curiosity and encourage students to explore new perspectives, ideas, and approaches, that may lead to new innovations, dialogue, and collaborations.

Offered: Fall
This course covers the basic service management functions of planning, organizing, leading, and controlling, as applied to project, team, knowledge, group/department and global settings. Discussion of the strengths and weaknesses of engineers as managers, and the engineering management challenges in the global economy will also be featured. Emphasis is placed on the integration of engineering technologies and management. Students will master the basic functions in engineering management, the roles and perspectives of engineering managers, and selected skills required to become effective engineering managers in the new millennium. 

Offered: Spring
This course covers the fundamentals of cost accounting, financial accounting, financial management, and marketing management in order to prepare service managers to meet future challenges in the marketplace. Business cases are used to discuss technologies for promoting service innovations, globalization of both service industries and labor markets, and the impact of these emerging market forces on service enterprises and managerial functions in the new millennium. Because of its recognized importance, this course is offered by the School of Engineering and Applied Sciences. It may be taken by students as an acceptable elective toward their master's degrees in any engineering disciplines. For students pursuing a master's degree of engineering in engineering management with UB's industrial engineering department, this is a required core course. 

Offered: Based on Demand
This course introduces students to technical communication practices and genres as they operate in different workplaces, focusing primarily on effective communications as well as information management required of engineers and engineering managers in industry, government, and business. Students master essential skills they need to select, organize, and design information, use easy-to-read language, write clearly and concisely, and adapt communication to peers, employers, clients, customers, and other audiences. The course also covers communication management that might be required in a technical leadership position in industry, government, and business in inter/cross-cultural and international technical communication contexts.

Offered: Fall
This course provides students with the ability to apply and integrate concepts covered previously in the program to solve real engineering management problems. It is a case-oriented course that examines the role of the engineering manager as a strategic planner, policy maker, and problem solver. Six or seven topics are presented for discussion, analysis, and report. Each topic is supported by one or more case studies. Topics include such things as managing in a global environment, addressing tradeoffs in production and quality engineering, use of artificial intelligence in management, and effective use of enterprise resource planning.