Schedule of Classes

Techniques for handling uncertainties in engineering design: discrete and continuous random variables; expectations, probability distributions and transformations of random variables; limit theorems; approximations and applications.

Sampling distributions; parameter estimation, hypothesis testing; analysis of variance; regression; nonparametric statistics.

Basic manufacturing processes including casting, machining, forming and welding; current trends in manufacturing processes including polymer, ceramic and composite material processing, and electronic device fabrication; introduction to numerical control and computer integrated manufacturing. Recommended preparation: MASC 110 or CHEM 105aL or CHEM 115aL

Facilities layout and design: material handling and transportation; site selection and sourcing; supply chain management.

Introduction to linear programming; transportation and assignment problems; dynamic programming; integer programming; nonliner programming.

Stochastic processes; Markov chains; queueing theory and queueing decision models; probabilistic inventory models. Recommended preparation: ISE 330

Examine team formation and team dynamics including organizational behavior, group dynamics, psychology, and business management, all in the context of engineering development; decision-makig and negotiation. Open only to juniors and seniors,

Physiological systems and psychological characteristics; ergonomics; anthropometry; effects of the physical environment on humans; occupational safety and health; work methods.

Concepts in modeling data for industry applications. Designing and implementing robust databases. Querying databases to extract business intelligence; Global Enterprise Resource Planning with databases.

Business process integration is the core advantage of using ERP systems. Analyze, configure, and test business processes for a company from the ground up. Duplicates credit in former ITP 322.

Model design to simulate discrete event systems with basic input and output analysis using high order languages, applied to industrial systems analysis and design problems.

Impact of technology on work and organizational design; effects of automation; design of improvement programs; information infrastructures; teams; individual behavioral outcomes. Upper division standing.

Utilizing principles of economic analysis for choice of engineering alternatives and engineering systems. Pre-tax and after-tax economy studies. Upper division standing.

Essentials of human factors and computer interface for the design, development, implementation, and evaluation of integrated media systems.

Management and technical issues related to the security of ERP systems. Students will audit ERP systems and apply appropriate security controls.

Individual research and readings. Not available for graduate credit.

a: Preparation and development of the senior project proposal. Not available for graduate credit. Senior standing in industrial and systems engineering.

Group work on an industrial engineering design problem in an organization. Not available for graduate credit. Senior standing in industrial and systems engineering.

Course content to be selected each semester from recent developments in industrial and systems engineering and related fields.

Cost control, finance, and engineering economy for construction operations.

Comprehensive study of Six Sigma and Lean metrics, methods, and systems with emphasis on their application to health care services.

Modern industrial automation, numerical control concepts, programmable controllers, robotics, computer-process interfacing, automated process and quality control, flexible manufacturing systems, introduction to computer-integrated manufacturing systems. Prerequisite: computer programming and departmental approval.

Deterministic and stochastic demand systems with static/dynamic models. Practice in inventory management, computerized procedures, materials requirements planning, just-in-time production, Kanban systems.

Applying industrial and systems engineering skills to problems drawn from industry, while working in teams of 3-4 students. Teach project management skills and provide direct experience in managing and executing a group project.

Study of various aspects of integrated manufacturing and service enterprises including management, design and production functions, interfaces and related resources and information systems. Recommended preparation: manufacturing processes, probability, statistics, computer programming.

Conditions for optimality; nonlinear programming algorithms for constrained and unconstrained problems. Special problems such as quadratic, separable, fractional, geometric programming.

Planning data collection to investigate relationships between product/process design choices (materials, temperatures, etc.) and performance, empirical modeling to predict performance, identification of the best design choices. Recommended preparation: ISE 225

Principles of quality management, quality philosophies and frameworks, quality leadership and strategic planning, process management, and performance measurements.

Linear programming, integer programming, transportation and assignment problems, networks, dynamic programming, Markovian models and queueing.

Tree, path, flow problems, formulation and solution techniques. Methods for minimal cost flows. Applications.

Simulation techniques combined with probabilistic analysis for solving problems in inventory theory, queuing theory, financial engineering, decision analysis, and other fields having a stochastic element.

Design and management of engineering teams. Group decision-making, motivation, leadership, infrastructure requirements, performance measurement, team diversity, conflict, and integration.

This project-oriented course elaborates on the process of engaging creative thought, tools and techniques for invention, and issues involved in bringing inventions to the production phase. Graded CR/NC.

Introductory probability; Markov chains; Martingales and stopping times; Brownian motion; Ito's calculus and formula; Black-Scholes formula; arbitrage pricing and risk neutral measures; options.

Explores fundamental techniques such as recursion, Fourier transform ordering, dynamic programming for efficient algorithm construction. Examples include arithmetic, algebraic, graph, pattern matching, sorting, searching algorithms.

Economic evaluations of engineering systems for both government and private industry; quantitative techniques for evaluating non-monetary consequences; formal treatment of risk and uncertainty.

Decision making under risk conditions; utility theory; sufficient statistics; conjugate prior distributions; terminal and pre-posterior analysis; Bayesian statistics versus classical statistics.

Concepts underlying the economic analysis of engineering projects; applications to call and put options; utility theory and mathematical optimizations models; and simulation. Recommended preparation ISE 220 or an equivalent course in probability.

Measurement systems for performance analysis. Determination of performance metrics, analytical models, case studies. Cross-industry comparisons, measures for manufacturing and service systems, information and knowledge workers.

Identification, formulation, and solution of financial accounting problems in engineering enterprises. Legal context of financial decisions, process cost determination and allocation, financial reports, and reporting systems. Open only to graduate students.

Scientific principles and industrial practices defining how a team of stakeholders should collaboratively work together to reach agreement on complex engineering tasks. Open only to graduate students in engineering.

Computational research in music cognition, including computational methods for music analysis, such as the abstracting and extracting of pitch and time structures. Computational research in expressive performance, the manipulation of parameters (e.g., tempo, loudness, articulation) to focus attention, facilitate parsing, and create emotional affect. Open to graduate engineering students only. Recommended preparation: programming experience (C++ or Java), basic signal processing and music theory.

Coverage of various stages of simulation processes using a project and case study oriented approach; an introduction to available simulation tools and modern simulation concepts.

A fast-paced, project-based introduction to designing and implementing interactive web applications. Emphasizes skills for building engineering and market research applications requiring information gathering, analysis, representation.

The role of enterprise resource planning systems (ERPs) in an organization and the task of implementing and managing the IS function.

Management skills and tools for technology intensive enterprises. Life cycle analysis of technology from planning through exploitation, obsolescence and renewal.

Concepts of risk analysis, risk in engineered systems, environmental risk, security risks; fault trees, event trees, risk simulations; risks and decision-making. Recommended preparation: MATH 108 or MATH 116

Research leading to the master's degree; maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of Thesis.

Credit on acceptance of Thesis.

Credit on acceptance of Thesis.

Introduction to the mathematical foundations, numerical algorithms, and computational tools necessary for solving problems of optimization under uncertainty.

Reports on current departmental research; review of papers, proposals, and special projects; guest speakers. Required of all students enrolled in Ph.D. program. Open only to Ph.D. students. Graded CR/NC.

Reports on current departmental research; review of papers, proposals and special projects; guest speakers. Required of all students enrolled in Ph.D. program. Open only to Ph.D. students. Graded CR/NC.

Reports on current departmental research; review of papers, proposals, and special projects; guest speakers. Required of all students enrolled in Ph.D. program. Open only to Ph.D. students. Graded CR/NC.

Reports on current departmental research; review of papers, proposals, and special projects; guest speakers. Required of all students enrolled in Ph.D. program. Open only to Ph.D. students. Graded CR/NC.

Fundamental techniques for design and analysis of algorithms. Topics include: dynamic programming; network flows; theory of NP-completeness; linear programming; approximation, randomized, and online algorithms; basic cryptography. Recommended preparation: familiarity with algorithms and discrete mathematics.

Research leading to the doctorate. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.