The bachelor of science in industrial and systems engineering is a new program within the Watson School. The curriculum has been designed to seek engineering accreditation that can only be granted, as per the accreditation rules, after the first students have graduated. When obtained, the accreditation will be retroactive.
The curriculum provides a strong technical focus on industrial and systems engineering, combining a focus on engineering, manufacturing systems and business/information processing. Students at Binghamton enter the junior year from the Watson School Division of Engineering Design. The curriculum is also specially structured to enable transfer students to complete the program in accordance with the Two-Year Engineering Science Association (TYESA) agreement with community colleges in New York state.
The emphasis is on the application of engineering fundamentals with a balanced treatment of theory, design and experience or practice. Computer applications are integrated throughout the curriculum. During the senior year the student is allowed to select two electives. The senior year also has a primary focus on design; a two-course design sequence covers the concepts and terminates in a capstone design experience that is normally shared with industry.
The primary goal is to prepare the industrial and systems engineering bachelor of science graduate for a creative, lifelong engineering career based upon a thorough grounding in the fundamentals of engineering, a special focus on manufacturing systems and the integration of business/information processing.
To complete the BS degree in industrial and systems engineering, students must complete 63 credit hours in the upper division, as outlined below. The students must meet all of the University requirements for graduation, including the General Education Requirement. For more information, students should contact the Watson School advising office.
Junior Year/Semester I credits
ISE 311. Manufacturing Systems I 4
ISE 361. Analysis of Variability in Systems I 4
Engineering elective 3
Humanities or social science elective* 4
TOTAL 15
Junior Year/Semester II credits
ISE 312. Manufacturing Systems II 4
ISE 362. Analysis of Variability in Systems II 4
ISE 370. Industrial Automation and Control 4
Humanities or social science elective* 4
TOTAL 16
Senior Year/Semester III credits
ISE 420. Optimization and Operations Research 4
ISE 463. Project Analysis and Control 4
ISE elective 4
ISE 491. Systems Design 4
TOTAL 16
Senior Year/Semester IV credits
ISE 421. Modeling and Simulation 4
ISE 464. Elements of Fuzzy Logic and Fuzzy Set Theory 4
ISE elective 4
ISE 492. Systems Design Project 4
TOTAL 16
* from list provided by Watson advising
A minor in computer science is available for BSISE majors. Students may also apply for an extended program leading to dual degrees in industrial and systems engineering and computer science. For details, students should contact the Watson School advising office prior to registration.
The department offers multiple graduate-level degrees. The flexibility offered by the variety of programs helps students to follow their individual career paths. The two master of science programs available are the MS in systems science and the MS in industrial engineering. The department also offers two master of engineering degrees; one in industrial engineering and the other in systems engineering. Requirements for admission are different for each degree program, as each serves students with different backgrounds.
The programs have the flexibility required by part-time students, EngiNet (distance) students and full-time students. The MEng degree programs allow students the greatest flexibility within the systems science and industrial engineering areas and combines its offerings with several courses from other disciplines.
The graduate program in systems science provides the student with systems concepts, principles and methods for developing an ability to understand the nature of systems problems, as well as proficiency in actual systems problem solving. Involved are problem classes such as systems modeling and simulation, systems analysis and synthesis (systems design), as well as various problems associated with the simplification of overly complex systems to make them manageable.
The program emphasizes the complementary use of mathematical, computational and heuristic approaches to solving systems problems. Students learn to analyze assumptions under which various methods are applicable, with the aim of selecting methods that best fit the problem.
Students with a baccalaureate degree in any field may apply for admission to this program. The GRE is required. The student must maintain at least a B average in the following course of study:
NOTE: In special cases, a student may request an individualized program. If it deviates from the above requirements, it must be approved by the student's advisor and the department chair. SSIE 500. Computational Tools
SSIE 501. Introduction to Systems Science
SSIE 505. Introduction to Applied Probability and Statistics
SSIE 506. Systems Problem Solving
SSIE 520. Modeling and Simulation
SSIE 592. Professional Seminar (taken after 12 credit hours in the program)
Four approved electives, including a 600-level course; and
Termination requirement
Students needing preparatory work may be advised to take appropriate undergraduate courses.
Some Sample SequencesGeneral Systems Sequence.
The general systems sequence provides the student with firm conceptual foundations and tools for solving systems problems of universal nature through a well-organized general systems methodology. Emphasis is given to balanced use of mathematical and computational tools in the study of problems associated with complex systems.
In addition to the courses required of all students in the systems science specialization, the student takes the following courses:
| SSIE 606. Systems Problem Solving Workshop | 2 |
| SSIE 617. Fuzzy Sets, Uncertainty, and Information | 3 |
| or alternatively
SSIE 516. Fuzzy Set Theory and Information Theory |
2 |
| SSIE 650. Systems Optimization | 3 |
The systems engineering program is specifically designed for students with a BS in engineering, physics or math. The intent of the program is to focus on the systems engineers concern with the integration of all aspects of systems, both technical and managerial. This may typically include design, production, deployment, operation, maintenance, modification and retirement, all within constraints of time, cost, personnel and other resources.
The course requirements for the systems engineering sequence vary slightly from those for the standard systems science specialization. The program must be approved by the MSSS adviser, and should be based on the following guidelines:
SSIE 500. Computational Tools*
SSIE 501. Introduction to Systems Science
SSIE 505. Introduction to Applied Probability and Statistics*
SSIE 525. Principles of Systems Engineering
SSIE 526. Systems Engineering Tools and Techniques+
or
SSIE 520. Modeling and Simulation
SSIE 527. Systems Design and Human Interactions
Termination requirement
Three approved electives, including a 600-level course
* For students with adequate backgrounds, electives may be substituted for one or both of these courses with permission from the adviser.
+This course subject to final approval.
The PhD in systems science is described earlier under "Graduate Information."
The master of science in industrial engineering provides a balance of theory and practical knowledge for the practice of the profession or for advancement to a doctoral program with an emphasis on manufacturing systems. In recognition of the high concentration of industry in the Binghamton area, this program has been structured to serve both the full- and part-time graduate student. Taking advantage of this industrial resource allows the program to develop a realistic approach to integrating manufacturing systems.
The academic environment of the department may be enriched by the appointment of adjunct faculty members employed in local industry. Under appropriate circumstances, thesis and/or project activity may be carried out in industrial laboratories.
Holders of the baccalaureate degree in industrial engineering or a related field are invited to apply for admission to this program. The GRE is required. A student whose undergraduate degree is not in industrial engineering may be required to complete some preparatory study in addition to the requirements listed below.
The student must maintain at least a B average in the following plan of study:
1. Four required courses:
SSIE 561. Quality Assurance
SSIE 520. Modeling and Simulation
SSIE 510. The Science of Manufacturing
SSIE 512. Integrated Manufacturing Systems
2. Four courses in an area of specialization or from a list of approved courses outside the department.
3. Complete either of the following independent study options:
a. Thesis: oral presentation and defense of the thesis are required.
b. Non-thesis: with departmental approval, the thesis requirement may be replaced by two approved elective courses and a project. Normally allowed only for part-time students.
The master of engineering degree (MEng) in systems engineering is a program designed for those students interested in the design, analysis and management of systems. The program consists of four required courses in the SSIE department, four approved elective courses and a two-course sequence in engineering practice. The required courses are:
SSIE 505. Applied Probability and Statistics
SSIE 520. Modeling and Simulation
SSIE 525. Principles of Systems Engineering
SSIE 527. Systems Designs for Human Interaction
For more information on this degree, see the School-Wide Graduate Program section of this Bulletin.
The master of engineering (MEng) in industrial engineering equips graduates with the skills needed to be effective in industry.
All MEng students must complete four approved courses in industrial engineering and four additional approved elective courses that may be chosen from other disciplines toward a particular academic goal. The specific courses in the program must be approved by the students committee. In addition to these courses, the student must also complete the two-course sequence in Engineering Practice, which includes a practice-oriented project.
For more information on this degree, see also the School-Wide Graduate Program section of this Bulletin.
[ TOP ]
NOTE: Unless otherwise noted, undergraduate courses carry four credits.
ISE 311. MANUFACTURING SYSTEMS I
The major changes in the manufacturing process and the paradigm shifts in
production are studied. Includes a focus on design, process improvement,
inventory, teams, maintenance, planning and control systems. Course is the first
of two required for ISE majors. Prerequisites: EE 260 and ME 271 or equivalent.
ISE 312. MANUFACTURING SYSTEMS II
Second course in the manufacturing series. Covers the models, networking and
systems needed to design and manage a manufacturing enterprise. Topics include
MRP, Kanban, workstation design, facility design and other such manufacturing
topics. Prerequisite: ISE 311 or consent of department chair.
ISE 361. ANALYSIS OF THE VARIABILITY IN SYSTEMS I
Discussion of practical aspects of data collection and descriptive
statistics. Introduction to basic concepts of probability theory, Bayes theorem,
probability distributions, point estimation and confidence interval inference
from data, and test of hypothesis. Use of ANOVA. Discussion of regression and
correlation. Use of control charts. Concepts of tolerance and methods for
determining tolerances. Methods of off-line and on-line quality, determination
of design parameters, and tolerance design. Prerequisite: third-year standing in
ISE program or consent of the instructor.
ISE 362. ANALYSIS OF THE VARIABILITY IN SYSTEMS II
Review of ANOVA. Discussion of designing with experiments, including
standard design of full factorial experiments, confounding and aliasing, Taguchi
experiments, screening experiments and fractionalized factorial experiments.
Concepts of non-parametric tests. Methods of fault isolation and failure mode
analysis. Terms and concepts relating to reliability and evaluation of
reliability. Concepts of stochastic models, such as standby modeling and Markov
models. Use of approximate methods and methods of testing and accelerated
testing. Prerequisite: ISE 361 or permission of the instructor.
ISE 370. INDUSTRIAL AUTOMATION AND CONTROL
The different technologies employed to implement industrial automation are
studied. Includes sensors, industrial robotics, numerical control, programmable
logic controllers, machine vision and the implementation of on-line computer
control. Laboratory work is required. Prerequisites: third-year standing in ISE
or MATH 323, WTSN 212 and EE 260, or consent of department chair.
ISE 371. FUNDAMENTALS OF INTEGRATED MANUFACTURING
Concepts that help to integrate manufacturing systems are studied, including
group technology, flexible manufacturing systems, computer-aided process
planning, assembly line balancing, computer-aided design and manufacturing,
concurrent engineering, supply chain management, etc. Necessary infrastructure
and techniques critical to computer-based integration addressed, including
computer networking, databases and communication protocols within manufacturing.
Prerequisite: third-year standing in ISE or consent of department chair.
ISE 400. FUNDAMENTALS OF MATHEMATICS
Basic concepts of logic: truth tables, tautologies, valid arguments. Set
theory: subsets, product sets, cardinality, functions and relations. Basic
concepts of probability; real functions of real variable; derivative, integral,
limit, continuity; interpolation, approximation; vectors and matrices.
ISE 410. FUNDAMENTAL STRUCTURES
Covered concepts: sets, fuzzy sets, graph theory, trees, finite automata,
Turing machines, formal languages, algebraic structures, semigroups, monoids,
lattices, Boolean algebras, homomorphisms, isomorphisms, etc. Presented in terms
applicable to modeling and simulation in systems and computer science. This
class is available to undergraduate students as SSIE 310. Graduate students
complete additional assignments. Prerequisite: SSIE 400 or equivalent.
ISE 419. APPLIED SOFT COMPUTING 4 cr.
Covers relatively new approaches to machine intelligence known collectively
as soft computing. Introduces various types of fuzzy inference systems, neural
networks, and genetic algorithms, along with several synergistic approaches for
combining them as hybrid intelligent systems. The emphasis is on applications,
including modeling, prediction, design, control, databases and data mining.
Offered as a dual level (graduate/undergraduate) course with SSIE 519. The
undergraduate students are not required to do projects on the same level as the
graduate students, and are not required to place the degree of emphasis on
hybrids. Prerequisites: senior standing, basic knowledge of calculus and
discrete mathematics, and competence in at least one programming language, or
consent of the instructor.
ISE 420. OPTIMIZATION AND OPERATIONS RESEARCH
Operations Research (OR) is devoted to the determination of the optimal
course of action of a decision problem given resource restrictions. Following a
review of linear algebra, the student learns the following: how to model an
engineering problem mathematically, how to solve the problem to optimality and
how to perform sensitivity analyses on the results. Students learn Linear
Programming (LP), Integer Programming (IP), Dynamic Programming (DP) and
Branch-and-Bound (B&B) techniques. Special emphasis placed on the solution
of engineering decision making in the following areas: transportation models;
project/production scheduling; inventory models; assignment problems.
Prerequisite: ISE 362 or consent of department chair.
ISE 421. MODELING AND SIMULATION 4 cr.
Model building, nature of simulation and material on the full range of
simulation activities, such as input analysis, output analysis, certification
and validation, and model animation. Includes random number generation;
distribution functions and random variates; applications of discrete event
simulation methods to queueing, inventory control, and production planning
problems; Markov processes, queuing theory and decision analysis. Prerequisite:
ISE 362 and ISE 420 or consent of department chair.
ISE 440. INTRODUCTION TO SYSTEMS SCIENCE
Includes the following: a general characterization of systems science as a
field of study; intellectual roots, philosophical assumptions and historical
development of the field; an overview of fundamental systems concepts,
principles and laws; and a survey of application areas of systems science and
its implications for other fields of study.
ISE 463. PROJECT ANALYSIS AND CONTROL
Covers the topics of project planning, economic decision making, costing and
pricing. Topics include network planning, present worth, annual cost, rate of
return, activity-based costing, inflation, price change. Prerequisite: ISE 362
or consent of department chair.
ISE 464. ELEMENTS OF FUZZY LOGIC AND FUZZY SET THEORY 3cr.
A simple introduction to basic elements of fuzzy logic and fuzzy set theory,
including an overview of classical logic and classical set theory. Included are
basic concepts and properties of classical sets and fuzzy sets, classical
relations and fuzzy relations, classical logic and fuzzy logic, and fuzzy
arithmetic. The practical utility of fuzzy logic and fuzzy set theory is
illustrated by describing selected applications in various areas of human
affairs.
ISE 473. ELECTRONICS MANUFACTURING
The objective is to understand facets of electronics manufacturing,
including the manufacture of printed circuit boards and the assembly of
electronic devices using surface mount technology. Materials and processes
related issues are studied. Course includes a laboratory.
ISE 491. SYSTEMS DESIGN
Covers the design process from the definition of requirements through the
final output. Focus is upon the design principles and design methodologies used
to ensure a quality outcome. Prerequisites: ISE 311 and 362, or consent of
department chair.
ISE 492. SYSTEMS DESIGN PROJECT
The termination design project for the undergraduate degree. Students are
expected to work in teams to provide solutions through design. Prerequisite: ISE
491 or consent of the department chair.
ISE 497. INDEPENDENT STUDY every sem., var. cr.
Individual study under direct supervision of a faculty member.
Prerequisites: approval of proposed subject by the faculty member and department
chair.
NOTE: Unless otherwise noted, graduate courses carry four credits.
* Pending Graduate Council approval.
SSIE 500. COMPUTATIONAL TOOLS
Prepares the student for the computer demands and opportunities involved in
the design, analysis, modeling and simulation of complex systems. Each student
develops a specific artificial life system demonstrating complex behavior. This
system serves as the focal point for investigating modern tools associated with
advanced research. Students develop proficiency in using object-oriented
paradigms, contemporary programming languages (Java, C++, Smalltalk), symbolic
computation (Mathematica, Maple, MathCad), spreadsheet analysis (Lotus, Excel,
QuattroPro), discrete and continuous simulation languages (SIMAN, ARENA, Stella)
and the Internet as research tools to enhance their computer modeling techniques
and understanding. Emphasis is placed on integrating the results of these tool
applications to produce a comprehensive, professional report and presentation
using word processing and presentation graphics packages. Prerequisite: graduate
standing or consent of the department chair.
SSIE 501. INTRODUCTION TO SYSTEMS SCIENCE
Course includes: a general characterization of systems science as a field of
study; intellectual roots, philosophical assumptions and historical development
of the field; an overview of fundamental systems concepts, principles and laws;
and a survey of application areas of systems science and its implications for
other fields of study.
SSIE 505. APPLIED PROBABILITY AND STATISTICS
Basic concepts in probability and statistics required in the modeling of
random processes and uncertainty. Bayes formula, Bayesian statistics,
independence of events; random variables and their descriptive statistics;
distribution and moment generating functions; Bernoulli trials, normal binomial
hypergeometric, Poisson, exponential and multinomial distributions, Chebyshevs
theorem; sampling, confidence intervals, estimation of parameters, Students-t,
gamma X squared and F distributions; hypothesis testing, contingency tables,
goodness of fit, non-parametric statistics, curve fitting, least squares,
regression, correlation; maximum entropy principle. Prerequisites: SSIE 400 and
one year of calculus.
SSIE 506. SYSTEMS PROBLEM SOLVING
A comprehensive conceptual framework for systems problem solving is
introduced. Methods applicable to broad classes of problems discussed.
Prerequisites: SSIE 410 and 505, and CS 340 or equivalent.
SSIE 510. THE SCIENCE OF MANUFACTURING
Manufacturing has become increasingly critical to our standard of living and
to our competitive market position. Little has really been published and
analyzed as to the underlying science of manufacturing. This course studies the
manufacturing literature and the manufacturing process and investigates the
underlying principles that govern manufacturing. Prerequisite: SSIE 505 or
equivalent.
SSIE 511. ADVANCED PRODUCTION AND SCHEDULE CONTROL
Production scheduling and control. Design/production interface, bills of
material, engineering revision control and general concepts of production
planning and control for the engineer. Prerequisite: SSIE 510 or consent of
department chair.
SSIE 512. INTEGRATED MANUFACTURING SYSTEMS
Integration of equipment, people and information required in total
manufacturing systems. Product/process design, planning and support, procurement
support, software/hardware. Prerequisite: graduate standing or consent of
department chair.
SSIE 513. ADVANCED DESIGN FOR MATERIAL SYSTEMS
Material and material-related costs represent as much as one-half to
two-thirds of todays manufacturing cost. The focus for a competitive
manufacturing enterprise must be upon controlling and reducing these costs. This
control and reduction is brought about through an analysis of the total system,
from purchase through the facility and out to the customer. Specific topics
include relationship buying, just-in-time, workstation design, handling systems
design, facility design and distribution systems. Prerequisite: SSIE 510 or
consent of department chair.
SSIE 516. FUZZY SET THEORY AND 2 cr. INFORMATION THEORY
Overview of basic concepts of fuzzy set theory. Difference between
probability theory and fuzzy set theory. Foundations of information theory.
Uniqueness of information measures, maximum and minimum principles of
information, fundamental properties of information. Prerequisites: SSIE 500 and
505, or equivalents.
SSIE 517. FUZZY SETS, UNCERTAINTY AND INFORMATION
Same subject areas as SSIE 516 (Fuzzy Set Theory and Information Theory),
but some topics are covered at greater depth.
SSIE 518. GENERALIZED INFORMATION THEORY
Several new theories of uncertainty are introduced, including possibility
theory, evidence theory and fuzzy set theory. Measures of information based on
uncertainty reduction within these theories are examined in detail and compared
with the classical measure of information formulated within the framework of
probability theory (the Shannon entropy). Three important principles of
uncertainty-based information are introduced, and their role in classical
science, systems science, computer science, engineering and other areas of human
affairs is discussed. Prerequisites: calculus and basic probability theory
(e.g., SSIE 505).
SSIE 519. APPLIED SOFT COMPUTING 4 cr.
Covers relatively new approaches to machine intelligence known collectively
as soft computing. Introduces various types of fuzzy inference systems, neural
networks and genetic algorithms, along with several synergistic approaches for
combining them as hybrid intelligent systems. The emphasis is on applications,
including modeling, prediction, design, control, databases and data mining.
Offered as a dual level (graduate/undergraduate) course with ISE 419. The
undergraduate students are not required to do projects on the same level as the
graduate students, and are not required to place the degree of emphasis on
hybrids. Prerequisites: senior standing, basic knowledge of calculus and
discrete mathematics, and competence in at least one programming language, or
consent of the instructor.
SSIE 520. MODELING AND SIMULATION 4 cr.
Approaches, methodologies and specific tools required in the modeling of
complex systems. Stochastic systems, discrete event simulation, experimental
statistics and elementary queuing theory. Dynamic behavior of continuous models.
GPIs, Stella II, SAS, Minitab, SPSS or JMP. Decision, utility and game theory.
Meta-modeling, adaptive linear machines. Sensitivity analysis, validity and
precision of model. Prerequisites: SSIE 410 and 505 and CS 541, or equivalents.
SSIE 521. ANALYSIS OF SIMULATION RESULTS
Ability to analyze, understand and control complex manufacturing systems is
enhanced through usage of simulation techniques. Topics include model
development, theory, model validation, evaluation and analysis of the results,
etc. Major emphasis on projects that are development models for actual
manufacturing systems. Prerequisite: graduate standing or approval of department
chair.
SSIE 525. PRINCIPLES OF SYSTEMS ENGINEERING
Provides the student with the basic principles of systems engineering
applied in transforming client requirements into an operational system. Topics
cover the full system life cycle: planning, integrated product/process
development, system architecture and design, modeling, requirements analysis,
development, integration, test and evaluation. Specialized concepts involved in
engineering complex systems are reinforced through case studies and student
exercises. Prerequisite: graduate standing or permission of instructor.
SSIE 530. ANALYSIS OF PHYSICAL SYSTEMS I
Modeling and analysis of physical systems described by continuous functions
of single independent variable. Mathematical models formulated; methods for
solution presented. Emphasis on relationships among original system,
mathematical model and mathematical solution. Prerequisites: one year of
calculus and some ordinary differential equations.
SSIE 531 (also ME 530). MAN-MACHINE SYSTEMS
A systems engineering characterization of the human operator and the
interaction with simple and complex machines, such as airplanes and ground
vehicles. Topics include human perception, information measurement, manual
control and mathematical modeling of the human operator. Modern control theory
is employed to characterize the man-machine system. Prerequisite: BS in
engineering or approval of department chair.
SSIE 535. ANALYTICAL METHODS
A survey and discussion of some of the more important and useful analytical
methods for analyzing a wide variety of engineering and scientific problems.
Topics include solution of differential equations, including methods for linear
equations, power series, eigenfunction expansions and separation of variables;
topics in multivariable calculus, including vector analysis; and selected topics
in linear algebra, integral transforms and functions of a complex variable. Each
of the methods is introduced in the context of real, applied problems and then
illustrated with typical "real world" applications. Prerequisite: two
semesters of calculus.
SSIE 536. NUMERICAL MODELING OF PHYSICAL PHENOMENA
Efficient and effective method for solving differential equations
numerically. Single and multistep methods for initial value problems for
ordinary differential equations; matrix and shooting methods for two-point
boundary value problems; and shooting methods for eigenvalue-eigenfunction
problems (for resonant frequency and mode shape calculations). Finite difference
methods for partial differential equations, including the heat, wave and
potential equations. Explicit and implicit methods, method of characteristics,
Lax-Wendroff schemes and various methods to accelerate the convergence of the
approximate solutions. Considerable emphasis is placed on the interpretation of
the numerical solutions in terms of systems they model and the qualitative (as
well as quantitative) insight they provide.
SSIE 541. JUSTIFYING NEW TECHNOLOGY
Traditional methods are reviewed against changes in manufacturing.
Estimating, product cost vs. utilization, risk, sensitivity analysis and
decision modeling. Prerequisite: basic course in engineering economics or its
equivalent, or approval of department chair.
SSIE 545. HEURISTIC PROBLEM SOLVING
Concept of problem as cognitive dissonance. Methods of problem recognition,
definition, solution, implementation, refinement. Intuitive approaches: analysis
and syntheses; Meta-problem solving development of strategies appropriate to
problem type; inventive thinking: deferred judgment, metaphorical and visual
thinking, finding order in chaos. Prerequisite: curiosity of creative process
and genuine desire to innovate.
SSIE 546. THE PSYCHOLOGY OF PROBLEM SOLVING
Current topics in cognitive science, including perception, learning, pattern
recognition, creativity, artificial intelligence, neural networks, brain theory,
evolution of function.
SSIE 550. INTRODUCTION TO SYSTEMS OPTIMIZATION
Basic course in engineering optimization with emphasis on algorithms and
applications. Topics: single variable optimization, multivariable optimization,
linear programming (formulation, simplex method, interior point methods,
sensitivity analysis, applications), constrained optimization (LaGrange
multipliers, Kuhn-Tucker conditions, direct search methods) and, time
permitting, quadratic programming and linearization methods. Students gain
enough proficiency to build optimization models of practical problems and solve
them using tools learned in class. Use of available optimization computer codes.
SSIE 555. AUTOMATED SYSTEMS
Basic elements of automating a manufacturing process. Programmable logic
controllers, data networks, parts orientation and feeding, robotics and systems
integration. Prerequisite: SSIE 512 or approval of department chair.
SSIE 556. EXPERT SYSTEMS IN ELECTRONICS PACKAGING
Role of artificial intelligence-based expert systems in manufacturing as
related to electronics packaging domain. Expert systems design and development
as applied to electronics packaging. Knowledge acquisition and representation
techniques solution spaces and search techniques, inference and deduction
mechanism, and design and development of prototype systems. Prerequisite:
graduate standing or approval of department chair.
SSIE 561. QUALITY ASSURANCE FOR ENGINEERS
Statistical quality control, designing for quality, process control, vendor
and customer quality issues, quality costs and production. Prerequisites: BS in
engineering (any field), and probability and statistics coursework, or consent
of department chair.
SSIE 562. RELIABILITY
Reliability networks, failure mode and effect analysis, apportionment, fault
trees and human reliability. Prerequisites: SSIE 561 and probability and
statistics, or consent of department chair.
SSIE 566. DESIGNING WITH EXPERIMENTS
Basics of applying statistical design, and the design function, statistical
experimental design, control of experimental setting, Taguchi methods and
analysis of results. Prerequisites: SSIE 561 and 505 or equivalents, or approval
of department chair.
SSIE 570. BIOLOGICAL SYSTEMS THEORY
Models of biological organizations that complement structural, analytic
concepts of molecular biology, and abstract, mathematical concepts of theories.
Extension of these principles to ecological, social and cognitive levels of
biological organizations.
SSIE 571. MODELING OF BIOLOGICAL SYSTEMS
Computer-based modeling of biological systems such as protein folding,
protein biosynthesis, aggregation of cells into tissue, self-assembly of
bacteriophages, origin of life and evolution, and neuron systems. Prerequisites:
SSIE 400 or equivalent and equivalence of sophomore physics.
SSIE 575. SYSTEMS DESIGN
Systems approach to design process. Complex, poorly defined
interdisciplinary problems. Design viewed as problem solving and opportunity
development activity. User identification and satisfaction, adaptive design,
design by attribute, design consciousness, models of design process.
Prerequisite: open to students with advanced standing or professional
experience.
SSIE 576 (also ME 571). MANUFACTURING PROCESSING I
Equilibrium and non-equilibrium microstructure arising from liquid-solid
processing of materials. Casting of metal alloys, fusion welding of metals,
injection molding of polymers, and brazing/soldering of metals. Prerequisite: BS
in engineering or equivalent, or consent of department chair.
SSIE 577 (also ME 572). MANUFACTURING PROCESSING II
Role of mechanical and thermal forces on solid state fabrication of
materials; extrusion, forging, particulate (powder) processing, rolling, sheet
forming, wire drawing, heat treating and sintering. Prerequisites: strength of
materials and heat transfer, or consent of department chair.
SSIE 578. PROCESSES FOR ELECTRONICS MANUFACTURING
The electrical content of manufactured products is increasing in all areas.
To prepare the engineer for manufacturing these electrical assemblies, this
course covers topics in soldering, wire bonding, TAB, printed wiring board
production, PCB assembly and population processes (through hole and SMT) and
associated environmental issues. Prerequisite: undergraduate course in
manufacturing processes, related experience or consent of department chair.
SSIE 580. SPECIAL TOPICS 1-4 cr.
Topics vary from semester to semester.
SSIE 590. SPECIAL TOPICS INDUSTRIAL ENGINEERING SPECIAL PROBLEM
This course is based upon a basic understanding of industrial engineering.
It varies and covers decision making in industrial or manufacturing engineering
situations. Major emphasis is usually in manufacturing systems. Prerequisite:
consent of department chair.
SSIE 592. PROFESSIONAL SEMINAR 2 cr.
Weekly seminar course conducted by faculty and outside speakers. Topics of
current research. Each student goes into details of at least one of topics of
seminar; through term paper, demonstrates in-depth understanding of topic to
satisfaction of faculty. Prerequisite: student should be in last semester of
masters program and have completed 12 credit hours.
SSIE 594. INDUSTRIAL INTERNSHIP
Industrial engineering, systems science and other professional experience.
Daily log book, memo progress reports and a formal final report required. The
internship may replace no more than one lecture course for the MSIE or MSSS
degree. Prerequisite: consent of department chair.
SSIE 595. TERMINATION PROJECT: SYSTEMS SCIENCE 2 cr.
Project acceptable both to student and to a faculty committee. Inquire at
Watson advising office to complete proper documentation. Prerequisite: consent
of instructor and committee members.
SSIE 597. INDEPENDENT STUDY 1-4 cr.
Independent study supervised by department faculty member. Student must
obtain consent of instructor, who then determines description of study program,
number of credits, frequency of meetings, location.
SSIE 598. MSIE PROJECT
Literature review, manufacturing system development or other projects as
defined by the project committee. Preparation of formal bound report for
department library.
SSIE 599. THESIS RESEARCH 6 cr.
Training in the methods of research. Varied computer modeling, hardware
development and experimentation as determined by the MSIE thesis committee. Oral
examination required (eight credits total). Bound thesis goes in University
Libraries.
SSIE 606. SYSTEMS PROBLEM SOLVING WORKSHOP 2 cr.
Project-oriented course based on material covered in SSIE 506. Specific
projects selected on basis both of interests of individual students and
composition of group. Prerequisite: SSIE 506.
SSIE 612. ADVANCED TOPICS IN INTEGRATED MANUFACTURING
The continual need to improve quality and productivity and remain
competitive in a global market requires the comprehensive integration of people,
equipment, computers and information within a manufacturing systems engineering
framework. This course studies manufacturing integration issues with a special
focus on integrating elements such as process planning, group technology,
concurrent engineering, product quality, cost analysis, flexible manufacturing,
inventory control, information flow and management, and global
computer-integrated manufacturing (CIM) concept. Prerequisite: SSIE 512 or
equivalent, or consent of department chair.
SSIE 617. FUZZY SETS, FUZZY LOGIC AND FUZZY SYSTEMS
Course consists of two parts. The first part covers fundamentals of fuzzy
set theory and the associated fuzzy logic. The second part is devoted to
applications of the theory. Topics of the theoretical part include basic
concepts of fuzzy set theory and fuzzy logic; representations of fuzzy sets;
extension principle that facilitates fuzzifications of classical mathematical
concepts; aggregation operations on fuzzy sets; the concept of a fuzzy number
and arithmetic operations on fuzzy numbers; fuzzy relations; fuzzy relation
equations; basic ideas of fuzzy logic; possibility theory based on fuzzy sets;
and information aspects of fuzzy sets. In the application part, methods of
constructing fuzzy sets in various application contexts are overviewed and
representative applications of fuzzy sets and fuzzy logic are examined. The
application areas covered include systems science; approximate reasoning in
expert systems; database and information retrieval systems; pattern recognition
and image processing; decision making; medicine; economics; psychology; and
various areas of engineering. Prerequisites: SSIE 505 or equivalent and calculus
and discrete mathematics, or consent of instructor.
SSIE 618. FUZZY MEASURES: THEORY AND APPLICATIONS
Provides the student with a frame of the general theory of fuzzy measure. It
includes some advanced knowledge on set theory (such as atom, s-compact, etc.),
basic concepts of classical measure and fuzzy measure, structural
characteristics of fuzzy measure, extension of fuzzy measure, concepts of
"almost" and "pseudo-almost" on fuzzy measure space,
measurable functions and convergence of their sequence on fuzzy measure space,
concept and properties of fuzzy integral, convergence theorems of sequence of
fuzzy integrals, application of fuzzy integral in synthetical evaluation.
Students are asked to explore applications of these and related concepts in
their areas of interest and write a term paper.
SSIE 620. ANALYSIS OF COMPLEX SYSTEMS
Techniques for and their applications to modeling and analyzing complex
systems. Decision trees, graph theory, time series and forecasting, system
identification, and nonlinear optimization, optimal allocation of resources,
cluster analysis, queuing theory, analysis of specific complex systems.
Prerequisites: SSIE 505, familiarity with simple differential equations.
SSIE 630. NEURAL NETWORK AND GENETIC MODELS 4 cr.
The use of autonomous self-organizing models in deducing complex systems
properties, behavior and relations; intelligence, learning, adaptation and
emergence in artificial systems; perceptions and threshold logic units,
discriminant functions, general non-parametric training; committee, piecewise
linear, layered and parametric machines; evolutionary programming, genetic
algorithms and satisficing vs. optimizing search strategies.
SSIE 632. PERTURBATION METHODS
Course focuses on application of perturbation methods to problems in
engineering mechanics. Regular perturbation expansions, method of mathed (and
composite) expansions, and method of multiple time scales are applied to
problems drawn from such areas as vibrations, fluid mechanics, heat conduction,
solid mechanics. Emphasis is on understanding the various methods discussed
(e.g., what method applies to what kind of problem, what each method does and
does not do, etc.) with applications used to illustrate the ideas.
Prerequisites: two semesters of calculus and a course in ordinary differential
equations.
SSIE 645. STATISTICAL MODELING WITH IMPRECISE PROBABILITIES
To deal with the uncertainty and the indeterminacy in systems, this course
covers a new and increasingly important mathematical theory of imprecise
probabilities, including upper probabilities and lower probabilities, based on
three fundamental principles: avoiding sure loss, coherence and natural
extension. Some useful models and strategies for assessing imprecise probability
are introduced, and some applications to probabilistic reasoning, statistical
inference and decision are discussed. Prerequisites: SSIE 505 and 516 or 517.
SSIE 650. SYSTEMS OPTIMIZATION
Broad spectrum of models and methods for systems optimization. Motivating
examples; classical constrained and unconstrained methods; search techniques;
linear programming; network and transportation systems, introduction to integer
programming. Prerequisite: SSIE 520 or one year of calculus.
SSIE 656. ARTIFICIAL INTELLIGENCE IN MANUFACTURING
Artificial intelligence applied to scheduling, inventory control, process
planning, maintenance; design and development of prototype systems; search
techniques, knowledge representation. Prerequisite: SSIE 551 or equivalent, or
consent of department chair.
SSIE 660. STOCHASTIC SYSTEMS
Discrete-state Markov chains; exponential and Poisson processes; reliability
technology; birth and death processes; queuing models; renewal theory;
continuous random variables; Kalman filters. Emphasis on applications.
Prerequisite: SSIE 555 or equivalent.
SSIE 661. ADVANCED ISSUES IN QUALITY
The topic of quality has taken more and more of a critical nature for
manufacturing systems. This course has two components. The first component is a
practical application of the concepts of quality, including the design and
execution of experiments in a real setting. The second component is the analysis
and study of future issues in the field of quality, such as the development of
loss equations, cost of high quality, and people and high quality. Prerequisite:
SSIE 566 (Designing with Experiments) or a general design of experiments course.
SSIE 670. SELECTED TOPICS IN COGNITIVE SCIENCE
Topics focus on current approaches to brain models and machine intelligence
and on the different criteria that are used to evaluate such models. These
approaches include programmable rule-based symbol systems (computationalism),
coherent, distributed networks (connectionism) and models based on
neurophysiology of simple and complex organisms. Special attention is given to
the evolutionary and developmental constraints on the many functions of nervous
systems and brains. Prerequisite: second-year graduate level course.
SSIE 680. ADVANCED SPECIAL TOPICS 1-4 cr.
Variable content, credit hours, prerequisites. When offered, covered topics,
credit hours, prerequisites, text specified. May be repeated for credit with
consent of instructor. Prerequisite: to be determined.
SSIE 697. ADVANCED INDEPENDENT STUDY
Supervised by department faculty member. Student must obtain consent of
instructor, who then determines description of program, number of credits,
frequency of meeting and location. Appropriate paperwork must be submitted to
complete registration.
SSIE 698. PRE-DISSERTATION 1-9 cr.
Exploratory research oriented toward PhD dissertation.
SSIE 699. DISSERTATION 1-12 cr.
Research for and preparation of PhD dissertation.
SSIE 700. CONTINUOUS REGISTRATION
Required to maintain matriculation through any spring or fall semester when
no other courses are taken. If minimal one-credit registration is not
maintained, student must reapply for admission.
SSIE 701. PRACTICUM FOR RESEARCH AND TEACHING ASSISTANTS every sem.
Required for all funded graduate assistants. Research or teaching supervised
by faculty adviser.
SSIE 707. RESEARCH SKILLS 1 cr./sem.
Development of research skills required within graduate program. May not be
applied toward course credits for any graduate degree.
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