Fall 2013

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.

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. Prerequisites: ISE 330 and ISE 460.

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

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

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.

Utilizing principles of economic analysis for choice of engineering alternatives and engineering systems. Pre-tax and after-tax economy studies. 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. Prerequisites: CSCI 101 and IOM 435 or ISE 382.

Rigorous modeling process leading from data to decisions. Explores theory and practice of Data Warehouses. Deriving Business Intelligence for strategic enterprise management.

Drivers and obstacles to the process of coordinating the flow of material/information along the logistics chain. Optimize the supply network, from raw materials to sales. Not available for major credit in Engineering except toward undergraduate and graduate programs offered by the Epstein Department of Industrial and Systems Engineering.

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 310 and IOM 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 435 and ISE 370 or ISE 470. Open only to Industrial and Systems Engineering majors.

Case based decision and statistical analysis. Framing engineering management situations with statistical methods. Experiments, regression, ANOVA, hypothesis, factor analysis. Open only to Fifth Year Seniors and Masterâ??s Students.

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

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.

Use of mechanical, electrical, and computer engineering, math, and computer science to design of high performance and sophisticated products and processes and systems involving mechatronic. Recommended preparation: Bachelor's degree in engineering or physical sciences, and preliminary knowledge of programming in C.

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.

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.

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

Design and management of engineering teams. Group decision-making, motivation, leadership, infrastructure 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.

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. Prerequisites: EE 441 and EE 464 or EE 465 or EE 503. Recommended preparation: EE 562a or EE 562b or ISE 538.

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.

Students will identify, formulate and resolve legal, financial and ethical issues affecting innovation in engineering organizations including legal structures, financing and intellectual property rights. Open only to graduate students.

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.

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.

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

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.

Course content will be selected each semester to reflect current trends and developments in the field of industrial and systems engineering.

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 Masterâ??s Students.

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.