Industrial and Systems Engineering (IE)

Introduces students to the field of industrial engineering, and the IE department at UB, through a series of real life IE case studies and examples.

Principles and methodologies of statistical inference; methods of data analysis; point and interval estimation; tests of hypotheses; correlation and regression. Introduces analysis of variance methods.

Applied concepts of economic decision making, including present worth analysis, cash-flow equivalence, replacement analysis, equipment selection. Open to students in any discipline.

The purpose of this course is to provide students with the knowledge and skills necessary to manage, manipulate, analyze, and derive insights from large data sets using IE related computational tools. Data and problems will be representative of typical problems faced by Industrial Engineers across a wide variety of industries including manufacturing, service, healthcare, and transportation industries.

Focuses on the interactions of humans with tasks, equipment, and the environment as part of the workplace system. Students learn about human capabilities and limitations, applications of human factors/ergonomics principles to system design, methodologies for human-system analysis, and experimental design and data analysis applied to human factors problems.

Examines principles central to the planning of a production process. These include topics germane to the planning, scheduling, and control of production.

Basics of design, analysis, and selection of manufacturing facilities and material-handling equipment. Approaches to analyzing manufacturing and material-handling systems. Applications of computers in modern facilities design and material handling, emphasizing material-flow control and storage. Economic justification models for manufacturing and material-handling systems.

Concepts of operations research methodology including objective functions, theories of value, and optimization. Elementary mathematical models of reliability, Markov decision processes, waiting-line models with Poisson arrival and exponential service, game theory, and decision analysis.

Continuation of IE 373, including uncertainty, risk, and the probabilistic approach in operations research. Topics include elementary mathematical models of decision analysis, stochastic processes (Markov chains and Markovian decision processes), queueing (waiting line) theory and applications of queueing.

Basic and important principles in computer-integrated manufacturing (CIM). Based on an understanding of modern production and manufacturing systems, the course further introduces to students the use of computers for the integration of all functional areas in a manufacturing enterprise. Topics include computer-aided design (CAD), geometric models and data structures, computer-aided process planning (CAPP), group technology (GT), computer-aided manufacturing (CAM), and database applications in manufacturing.

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. This course is dual-listed with IE 508.

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).

A review and discussion of concepts, models, tools and applications pertaining to social network formation and behavior. This course is dual-listed with IE 511.

Decision analysis that extends the domain of decision-making problems from those considered in traditional statistical hypothesis testing scenarios: modeling decisions, which emphasize structuring decision problems using techniques such as influence diagrams and decision trees; modeling uncertainty, which covers subjective probability assessment, use of classical probability models, Bayesian analysis, and value of information; and modeling preferences, which introduces concepts of risk preference, expected utility, and multi-attribute value and utility models. This course is dual-listed with IE 512.

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.

Integrates and applies knowledge from multiple aspects of industrial engineering to analyze and solve real-world ID problems. Combines knowledge of production planning, facilities design, operations research, and human factors.

The course is designed for engineering undergraduate/graduate students who are interested in furthering their knowledge in green engineering techniques. The approaches presented in this class are from the levels of manufacturing, machine and systems, as well as the overall product lifecycle and supply chain perspective. The main goal of this course is to help students understand the importance of green and sustainable manufacturing. The course will discuss some practical aspects of sustainability and green engineering with students and encourage them to look at the processes and systems around them more carefully and think about opportunities to improve, reuse, replacement and reduction of the environmental impacts of the processes, systems, and behaviors. This course is the same as MAE 421 and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.

This course focuses on recent and future improvements in healthcare services through key applications of operations research (OR). In addition to developing a conceptual understanding of decision making in healthcare from a practical standpoint, the course also develops technical skills in optimization, stochastic processes and game theory. Lectures cover book chapters and journal articles from the intersection of OR and healthcare literature. Paper presentations and term projects help students master basic OR techniques on emerging healthcare problems.

Familiarizes students with the principles and practice of use- and user-centered design of human-computer interfaces. By the end of the course, students are able to identify, describe, and implement a subset of methods appropriate for requirements gathering, user and task modeling, prototype development, and user test plan development, implementation, and analysis. Additionally, students are able to apply principles in user-centered design to interface design.

This course provides an overview of occupational ergonomics, the science that uses knowledge of human capabilities and limitations to improve the design of the work environment. It will cover relevant human anatomy-my, physical human capabilities in terms of physiology and biomechanics, and will use this information to practice ergonomics in occupational and research settings. This course is the same as BE 436.

Overall view of the professional field, particularly emphasizing the role of the human, and human factors, in safe working systems. Provides information relevant to understanding basic concepts associated with systems safety, such as the legislative environment, management culture, and sources of workplace hazards; understanding the need to consider the human when designing safe, complex systems; and analyzing a work system to predict its hazards and select possible interventions.

Studies special areas of interest to students and instructor. Content varies from section to section and semester to semester. This course is dual-listed with IE 500.

Topics include the development of digital simulation models of complex systems using up-to-date software tools and methods.

Analyzes robots and robotic systems: kinematics, coordinate transform, numerical control, off-line programming, robot vision, and simulation of robotic systems.

The objective of this course is to provide working knowledge required to formulate and analyze problems related to the application of collaborative robots. This course will also provide methodologies for solutions to such formulated problems. This course is the same as MAE 490 and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.

A field experience working on an industrial engineering project in a manufacturing plant, hospital, library, police department, or similar location under the joint direction of a preceptor from industry and a faculty advisor. Projects are selected that integrate the material learned in academic courses. Requires a written report and an oral presentation.

This course is designed to allow students to gain credits for their Industrial Engineering degree while applying the skills and knowledge learned in an industry position. This is a variable unit course, up to three (3) credits. The number of credits to be earned is designated by the number of credits for which the student registers. For each credit hour, a minimum of forty-five (45) hours at the internship or jobsite must be completed.

Students collaborate with faculty research mentors on ongoing research of the faculty member or conduct independent research under the guidance of a faculty member.

Student directed independent learning activity guided & mentored by faculty. Students should discuss registration with the faculty member and have a plan in writing upon registering. Note that activities that are primarily research (vs. study or project based) should be conducted under the IE 498 number.