Fall 2020

A combination of plant tours, laboratory experiences, and lecture are used to introduce the philosophy, subject matter, aims, goals, and techniques of industrial and systems engineering.

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. Prerequisite: MATH 126.

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

Techniques for planning, budgeting, scheduling, obtaining resources, monitoring and control of projects in a corporate environment. Use of software to evaluate performance and assess risks. Prerequisite: ISE 225 and ISE 330.

Standardized methodology for problem solving, basic and advanced statistical tools for root cause identification and solution confirmation, key principles for quality management and organizational excellence. Prerequisite: ISE 225.

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

Design of Supply Chains. Product, distribution, transportation and site selection. Analysis and optimization of supply chain networks.

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

Design of facilities for operations and distribution. Product, process flow, material handling, and facility location techniques that lead to making good decisions for facilities layouts.

Data models for industry applications. Modeling and designing robust databases. Implementing and querying databases with SQL. Innovations in database applications.

Production planning, forecasting, scheduling, and inventory; computer integrated decision systems in analysis and control of production systems. Prerequisite: ISE 330.

Quantitative aspects of statistical quality control (process control, acceptance sampling by attribute and by variable, rectifying inspection), quality assurance and the management of QC/QA functions.

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. Prerequisites: CSCI 101 and ISE 225.

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

Planning and implementation of engineering information systems that interface with a large database. Emphasis is placed on web-based data entry and retrieval.

Methodology to store, organize, cleanse, harmonize and stage enterprise data for analytics. Report, visualize, slice and dice, forecast and predict trends.

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. Prerequisites: ISE 225 and ISE 460 and DSO 435 or ISE 382.

Group work on an industrial engineering design problem in an organization. Not available for graduate credit. Senior standing in industrial and systems engineering. Prerequisites: ISE 315 and ISE 435 and ISE 370 or ISE 470. Open only to Industrial and Systems Engineering majors.

Framing engineering management situations with statistical methods. Experiments, distributions, regression, ANOVA, hypothesis.

An introduction to a conceptual design framework with a logic foundation and a systematic process to carry out concept generation and improvement for innovative product developments.

Fundamental accounting principles, financial analysis and financial control, strategic management and a review of alternative project delivery methods.

Study of lean principles and practices as applied to various industries.

Improving operations, patient flow, quality and processes. Students will become familiar with methods for implementing change in health care settings such as hospitals or clinics.

Theories of management and their application to software projects. Economic analysis of software products and processes. Software cost and schedule estimation, planning and control.

Advanced concepts in production planning and scheduling including resource allocation, lot sizing, flow shop and job shop scheduling, workforce scheduling and assembly line balancing. Recommended preparation: prior knowledge of operations research and probability theory.

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.

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

Analytics for supervised and unsupervised statistical learning. Generalized linear models, discriminant analysis, support vector machines. Nonparametric classification, trees, ensemble methods, k-nearest neighbors. Principal components, clustering.

Linear Programming, Integer Programming, quadratic and nonlinear programming.

Data preprocessing, data cleaning, data summarization, data visualization, and predictive modeling for classification and regression; modeling dependencies using association rules.

Linear programming models for resource allocation; simplex and revised simplex methods; duality; sensitivity; transportation problems; selected extensions to large scale, multiobjective, and special structured models.

Portfolio construction and active portfolio management; investment strategies; machine learning models in quantitative finance; equity factor investing and general risk premia investing.

Random variables, stochastic processes, birth and death processes, continuous- and discrete-time Markov chains with finite and infinite number of states, renewal phenomena and queuing systems.

Methods and algorithms for automated text analysis; machine learning; predictive web data analytics; information retrieval; social media data; natural language documents and graphs.

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

Principles and practices of technology development and implementation, with application to products and systems in manufacturing and services.

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.

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. Prerequisite: ISE 500. Recommended preparation: An undergraduate course in engineering economy.

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.

Statistical methods for building intelligent and adaptive systems that improve performance from experiences; Focus on theoretical understanding of these methods and their computational implications. Recommended preparation: Undergraduate level training or coursework in linear algebra, multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required.

Introduction to modeling and analysis of stochastic systems, with an emphasis on discrete-event simulation of non-Markovian systems.

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.

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.

Advanced planning for data collection in order to investigate relationships between product/process design choices and performance; empirical modeling to predict performance; identification of the best design.

Doctoral course in optimization, the foundation for subsequent courses in mathematical programming. For first-year Ph.D. students and M.S. students who intend to pursue a Ph.D. Recommended preparation: One year of calculus and one semester of linear and matrix algebra.

Large scale optimization algorithms that arise in modern data science and machine learning applications. Stochastic Optimization, Accelerated Methods, Parallelization, Online Optimization, Randomized Linear Algebra

Current research, guest speakers in the field; review papers; guidance in preparing research proposals and special projects. Duplicates credit in the former ISE-650abc. Open only to Fifth Year Seniors and Master’s Students.

Fundamental techniques for design and analysis of algorithms. Dynamic programming; network flows; theory of NP-completeness; linear programming; approximation, randomized, and online algorithms; basic cryptography. Prerequisite: CSCI 570. Recommended preparation: familiarity with algorithms and discrete mathematics. Open only to doctoral students.

Standard techniques in the design and analysis of randomized algorithms and random structures. Topics include tail bounds, Markov Chains, VC-dimension, probabilistic method. Prerequisite: CSCI 570 or CSCI 670. Recommended preparation: Basic background in probability and linear algebra.

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.