USC 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. 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

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.

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.

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.

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.

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

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.

Comprehensive study of Six Sigma and Lean metrics, methods, and systems with their applications to manufacturing, services, quality improvement and management.

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.

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.

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.

Managing the process design, interfaces and resources of service and manufacturing systems, based on the state of their processes.

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.

Theory and methods of data analytics emphasizing engineering applications: multivariate statistics, supervised learning, classification, smoothing and kernel methods, support vector machines, discrimination analysis, unsupervised learning. Prerequisites: DSO 529.

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.

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

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.

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.

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.

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.

Reasoning under uncertainty, statistical directed and undirected graphical models, temporal modeling, inference in graphical models, parameter learning, decisions under uncertainty. Recommended preparation: An undergraduate level course in probability theory.

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 analytic methods for Markovian systems and discrete-event simulation of non-Markovian systems. Probability and statistics, including hypothesis testing and introductory computer programming.

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.

Applications of continuous time Markov chains, semi-Markov processes, martingales, random walks, and coupling techniques to models of industrial systems. Queuing, reliability, and optimization models. Prerequisite: ISE 538.

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 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.