Spring 2021

Engineering examples are used as a platform to introduce computer programming skills for problem solving; critical thinking skills.

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.

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.

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.

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.

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

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.

Modeling techniques used to inform decisions in public health, disease control, and hospital 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 computeraided design and manufacturing. Develop hands-on computational skills in team projects.

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.

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.

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.

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.

Consulting project concepts, frameworks, analytical tools, and managerial skills with a focus on the use of data analytics, design thinking and insight-driven frameworks.

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.

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, infrastructural requirements, performance measurement, team diversity, conflict, and integration.

Stochastic system models, Dynamic programming, Linear quadratic control, Kalman filtering and estimation, System identification, approximate dynamic programming methods, adaptive control, reinforcement and online learning.

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.

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.

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.

Built on the methodology of constrained optimization, a graduate-level course on equilibrium programming cast in the framework of finite-dimensional variational inequality and complementarity problems. Prerequisite: ISE 520 and ISE 536.

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.

Rational decision making; value and utility functions; distribution theory and copulas; value of information and control; multi-attribute utility; decision and game theory; behavioral research.

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.