Social network analysis is an emerging field in modern science. En route to accumulating knowledge and gaining understanding about social network structure and behavior, researchers across multiple domains engage in theoretical and applied investigations. This course is intended to review key concepts and findings with network perspectives on communicating and organizing. It will rely on scholarship on the science of networks in communication, computer science, economics, engineering, organizational science, life sciences, physical sciences, political science, psychology, and sociology, with the purpose of taking an in-depth look at theories, methods, and tools to examine the structure and dynamics of networks.
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).
Individual problems/research on topics as determined by students working individually with a faculty member.
Facilities design is a broad field. The traditional aspects that are covered in this course are facility layout and facility location, which remain relevant in production systems. Increase in online shopping has highlighted that need to study effective warehouse design and automated vehicle routing methods in a warehouse. These topics will also be covered. The focus on coverage of these topics is on using quantitative methods to tackle these problems. Material from recent research papers will be used to supplement the course material.
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.
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.
This course introduces the student to the fundamental principles of planning, designing, and analyzing statistical experiments. (This course is, at times, replaced by Statistics 526.)
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.
Familiarizes students with customer-focused, process and design six sigma quality management methods. This course emphasizes methodologies used in the identification and selection of high impact, customer-focused, quality improvement projects. Topics covered include leadership soft-skills, the mathematics behind six sigma metrics, project selection criterion, risk assessment, quality tools, and structured six-sigma problem-solving methodologies (DMAIC and DMADV).
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.
Revenue Management (RM), or Yield Management, is a set of operational tools for generating more revenue with resource allocations and/or dynamic pricing. In this course, we will cover the fundamental concepts of RM, with mathematical models and algorithms, including capacity control, network capacity control, overbooking, dynamic pricing, customer choice modeling, pricing under competition, estimation and forecasting. By the end of this course, students will be able to understand the basic principles of RM, build mathematical models and suggest proper computational solution methods.
This course aims to provide students with a general background of various statistical analysis techniques and data mining methods that are ued 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.
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.
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.
The course is designed for engineering graduate students who are interested in furthering their knowledge in decision making methods during the engineering design process. The Decision- Based Design (DBD) approach models design as a decision-making process with the aim of maximizing the performance of a designed artifact. The main question addressed in the course is "what design alternative is the best considering both the performance of the design artifact and the consumer preferences?" This course emphasizes the role of uncertainty in engineering design and reviews different approaches to decision-based design through discussing the strengths and weaknesses of each approach. Various topics including Robust Design, Suh's Design Axioms. Multi-Attribute Utility Theory, Discrete Choice Analysis, Simulation-based Approaches, and Analytical Techniques for Modeling Consumer Preferences and Choices will be discussed. Moreover, this course discusses the challenges that decision maker's irrationalities and cognitive biases can bring into the design process.
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.
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.
The primary objective of the course is to introduce graduate- and senior-level undergraduate students to the principles and methods underlying human-computer interaction and the design of effective computer interfaces. In contrast to the design of computer systems based primarily on technological constraints and capabilities, theories and methods in human-computer interaction emphasize the design of computer systems which are designed to support user 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.
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.
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.
Theories of accident causation. Development of a systems approach for collecting and analyzing accident data. Human reliability analysis and related techniques. Federal and state regulations and standards. Organization and management of a safety program in a company. Prevention of common safety hazards. Design of warnings and training.
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.
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.
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).
Independent research leading to MS Thesis.
An introduction to concepts of lean thinking including it's applications to manufacturing, new product development, supply chain, service, and administration. This course focuses on the deceptively simple lean principles that provide the foundation for productions system improvement. These lean concepts are extended to service industry applications and additional enterprise functions.
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.
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.
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.
A continuation of IE 575. Topics include discrete-time and continuous-time Markov chains, queuing theory, Bayesian statistical inference and classical statistics.
Analyzes robots and robotic systems, including the design of robot controllers, coordination of multiple robots, simulation of robotic systems, and optimization of robot task scheduling.
The research and case study oriented course will discuss a variety of topics in industrial engineering research in healthcare. Classes include lectures and invited guest speakers, student discussions, individual assignments and group research projects. Topics include the overview of the healthcare industry and the patient "flow," operations in hospitals, cost of healthcare and insurance, home care, healthcare IT, and global health equity.
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.
Recent offerings of this course have focused on cognitive engineering, applied work measurement methods, musculoskeletal epidemiology, and home health care.
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.
Application of field research methods to advance human factors and ergonomics (HFE) knowledge and practice in the workplace. The methods include those to evaluate human-machine system performance, the relationship between work demands and the physiological and psysiological effects on people in natural work settings. The class begins with a series of discussions on the fundamental principles of human performance measurement in the field. Field study research designs and work methods used in field research are covered via case examples from the scientific literature, and from my personal experiences as an ergonomics field research and consultant. In the final weeks of the course, students complete a project at a local company.
Overview of methods and techniques used to perform interdisciplinary research in the areas of home health and home healthcare. Students participate in discussions that center on the related scientific literature, perform independent research and work in teams on projects to learn about the current state of research and practice in home health and healthcare. Topics cover three themes: Environments for Health and Well-Being, Inegrated Assistive Technologies, and Home Health Informatics. The impacts of technological advancements in these areas are studied across a variety of home health issues covered throughout the semester.
This course provides an introduction to formal methods and formal verification and their application to Human Factors Engineering. The course will cover the following topics: basic mathematics used in computation theory and formal methods, automata theory, formal modeling and formal specification. It will also describe how these can be used to account for usability, mode confusion, normative and erroneous human behavior, and perception in human-interactive systems.
Independent research leading to PhD dissertation.
In-depth analysis of selected topics in Operations Research. Course content will focus upon particular interests of the students and the instructor.
Single and multivariate classical optimization and Kuhn-Tucker theory. Computational methods, including penalty function, barrier function, gradient, and cutting plane approaches.
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.
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.
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.
Both from a theoretical and practical perspective, Multiple Criteria Decision Makngt (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-prference 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).
This is an advanced graduate level course which will cover in-depth concepts of additive manufacturing (3-D 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.