Spring 2018

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.

Facilities layout and design: material handling and transportation; site selection and sourcing; supply chain management.Prerequisites: ISE 330 and ISE 460.

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

Systems as complex collaborative ensembles of interconnected components. Theory and practice of requirements, design, implementation, testing, deployment, operation, and disposal. Case studies from real projects.

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

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.

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

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.

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

Individual research and readings. Not available for graduate credit.

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.

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

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

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

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

Performance improvement projects within hospitals are undertaken by small teams collaborating with hospital staff. Study of tools to improve and optimize operations.

Study advanced concepts behind computational representations, algorithms, and mathematical foundations, and their applications in computer-aided design and manufacturing. Develop hands-on computational skills in team projects. Recommended preparation: bachelor's degree in industrial engineering; programming experience, C++ preferred.

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

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.

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.

The three pillars of analytics (predictive, prescriptive, and validation) together under one framework. Use of statistics, learning enabled optimization. Based on publicly available data sets.

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.

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.

Function, design, and use of modern data management systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm.

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.

Concepts and methods to analyze the environmental impacts of industrial systems, including life-cycle assessment, material flow analysis, design for environment and sustainable consumption.

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.

Concepts of risk analysis, risk in engineered systems, environmental risk, security risks; fault trees, event trees, risk simulations; risks and decision-making.

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.

An introductory PhD course on stochastic processes, covering such topics as conditional expectation, renewal processes, and discrete and continuous time Markov chains.

Convex sets, convex functions, structures of optimization problems, Lagrangian and conjugate duality. First and second order optimality conditions; applications in engineering and management. Recommended preparation: Calculus III and Linear Algebra.

Combinatorial optimization, particularly graph problems. Shortest paths, max flow, minimum cost flows, Spanning trees, matroids, submodular functions. Bipartite and general matchings, Polyhedral combinatorics, total unimodularity. Prerequisite: ISE 536. Recommended preparation: Familiarity with the theory of linear programming and with mathematical proofs; knowledge of linear algebra. Duplicates credit in ISE 532.

Stochastic linear and integer programming, multi-stage stochastic programming, application, models and algorithms. Recommended preparation: A first graduate course in optimization and the ability to program in a high level language are essential.

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.

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.