The Bachelor of Science (BS) degree in Industrial Engineering and Operations Research (IEOR) is designed to prepare students for technical careers in production or service industries. It provides a strong foundation for those headed for engineering management positions or for those intending to go on to specialized graduate study in operations research, industrial engineering, or business administration.
Students interested in Industrial Engineering and Operations Research may also be interested in the Operations Research and Management Science major in the College of Letters & Science. For further information on this program, please see the Operations Research and Management Science page in this Guide.
Course of Study Overview
The core of the program includes basic science, mathematics including probability and statistics, engineering optimization, and stochastic models. This forms the methodological foundation for upper division IEOR electives involving the analysis and design of production and service systems, information systems, and human work systems and organization, among others.
Accreditation
This program is accredited by the Engineering Accreditation Commission of ABET.
Admission to the Major
Prospective undergraduates to the College of Engineering will apply for admission to a specific program in the College. For further information, please see the College of Engineering's website.
Admission to Engineering via a Change of College application for current UC Berkeley students is highly unlikely and very competitive as there are few, if any, spaces that open in the College each year to students admitted to other colleges at UC Berkeley. For further information regarding a change of college to Engineering, please see the College's website.
Minor Program
The department offers a minor in IEOR. Students must have a minimum overall grade point average (GPA) of 3.0 and a minimum GPA of 3.0 in the minor's prerequisite courses in order to be considered for departmental acceptance into the minor.
For the minor to be added to the transcript, students must file the Confirmation of Completion of Minor form with the Office of Undergraduate Advising in 4145 Etcheverry Hall during the last semester in which they complete their last class for the minor.
Students must acquire fluent programming skills as demonstrated by completion of coursework in a high-level language such as Python, C, C++ or Java. This requirement may be completed by taking COMPSCI C8 or COMPSCI 61A or equivalent. The CS 9xx series self-paced courses are intended for those already skilled as programmers in a high-level language to learn a second language and thus are not appropriate for meeting this requirement. Junior transfer admits who have completed a programming course of 3 units or more in Python, C, C++, or Java have fulfilled this requirement.
Minor programs are areas of concentration requiring fewer courses than an undergraduate major. These programs are optional but can provide depth and breadth to a UC Berkeley education. The College of Engineering does not offer additional time to complete a minor, but it is usually possible to finish within the allotted time with careful course planning. Students are encouraged to meet with their ESS adviser to discuss the feasibility of completing a minor program.
All the engineering departments offer minors. Students may also consider pursuing a minor in another school or college.
General Guidelines
All minors must be declared no later than one semester before a student's Expected Graduation Term (EGT). If the semester before EGT is fall or spring, the deadline is the last day of RRR week. If the semester before EGT is summer, the deadline is the final Friday of Summer Sessions. To declare a minor, contact the department advisor for information on requirements, and the declaration process.
To be considered for the minor, students must have a declared major other than IEOR or
ORMS and submit the Minor Petition in IEOR.
Submit the Petition form (to the IEOR Student Services Office) when you have at least
four (4) of the minor requirements completed. Minor petitions are accepted year-round
on a rolling basis.
○ A minimum overall grade point average (GPA) of 3.0 and a minimum GPA of 3.0
in the prerequisite courses is required for acceptance into the minor program.
Students must have completed all course requirements.
● All courses taken to fulfill the minor requirements must be taken for graded credit.
● A minimum grade point average (GPA) of 2.0 is required for courses used to fulfill
the minor requirements.
● No more than one upper division course may be used to simultaneously fulfill
requirements for a student’s major and minor programs.
● Completion of the minor program cannot delay a student’s graduation.
Students taking the minor are also required to submit the IEOR Minor Completion Form
when they are enrolled in their last required course, by the last day of instruction of their
final term.
Students in the College of Engineering must complete no fewer than 120 semester units with the following provisions:
Completion of the requirements of one engineering major program study.
A minimum overall grade point average of 2.00 (C average) and a minimum 2.00 grade point average in upper division technical coursework required of the major.
The final 30 units and two semesters must be completed in residence in the College of Engineering on the Berkeley campus.
All technical courses (math, science and engineering) that can fulfill requirements for the student's major must be taken on a letter graded basis (unless they are only offered P/NP).
Entering freshmen are allowed a maximum of eight semesters to complete their degree requirements. Entering junior transfers are allowed a maximum of four semesters to complete their degree requirements. (Note: junior transfers admitted missing three or more courses from the lower division curriculum are allowed five semesters.) Summer terms are optional and do not count toward the maximum. Students are responsible for planning and satisfactorily completing all graduation requirements within the maximum allowable semesters.
Complete the lower division program before enrolling in upper division engineering courses.
Humanities and Social Sciences (H/SS) Requirement
To promote a rich and varied educational experience outside of the technical requirements for each major, the College of Engineering has a six-course Humanities and Social Sciences breadth requirement, which must be completed to graduate. This requirement, built into all the engineering programs of study, includes two reading and composition courses (R&C), and four additional courses within which a number of specific conditions must be satisfied. Follow these guidelines to fulfill this requirement:
Courses must be a minimum of 3 semester units (or 4 quarter units).
Two of the six courses must fulfill the College's Reading and Composition (R&C) requirement. These courses must be taken for a letter grade (C- or better required). The first half (R&C Part A) must be completed by the end of the freshman year; the second half (R&C Part B) must be completed by no later than the end of the sophomore year. Please see the Reading and Composition Requirement page for a complete list of R&Cs available and a list of exams that can be applied toward the R&C Part A requirement. Students can also use the Class Schedule to view R&C courses offered in a given semester. Note: Only R&C Part A can be fulfilled with an AP, IB, or A-Level exam score. Test scores do not fulfill R&C Part B for College of Engineering students.
The four additional courses must be chosen from the five areas listed in #13 below. These four courses may be taken on a pass/no pass basis.
Special topics courses of 3 semester units or more will be reviewed on a case-by-case basis.
Two of the six courses must be upper division (courses numbered 100-196).
One of the six courses must satisfy the campus American Cultures (AC) requirement. Note that any American Cultures course of 3 units or more may be used to meet H/SS
A maximum of two exams (Advanced Placement, International Baccalaureate, or A-Level) may be used toward completion of the H/SS requirement. View the list of exams that can be applied toward H/SS requirements.
Courses may fulfill multiple categories. For example, CY PLAN 118AC satisfies both the American Cultures requirement and one upper division H/SS requirement.
Courses numbered 97, 98, 99, or above 196 may not be used to complete any H/SS requirement.
The College of Engineering uses modified versions of five of the College of Letters and Science (L&S) breadth requirements lists to provide options to our students for completing the H/SS requirement. The five areas are:
Arts and Literature
Historical Studies
International Studies
Philosophy and Values
Social and Behavioral Sciences
Within the guidelines above, choose courses from any of the Breadth areas listed above. (Please note that you cannot use courses on the Biological Science or Physical Science Breadth list to complete the H/SS requirement.) To find course options, go to the Class Schedule, select the term of interest, and use the Breadth Requirements filter.
Class Schedule Requirements
Minimum units per semester: 12.0
Maximum units per semester: 20.5
Minimum technical courses: College of Engineering undergraduates must enroll each semester in no fewer than two technical courses (of a minimum of 3 units each, with the exception of Engineering 25, 26 and 27) required of the major program of study in which the student is officially declared. (Note: For most majors, normal progress will require enrolling in 3-4 technical courses each semester). Students who are not in compliance with this policy by the end of the fifth week of the semester are subject to a registration block that will delay enrollment for the following semester.
All technical courses (math, science, engineering) that satisfy requirements for the major must be taken on a letter-graded basis (unless only offered as P/NP).
Minimum Academic (Grade) Requirements
A minimum overall and semester grade point average of 2.00 (C average) is required of engineering undergraduates. Students will be subject to dismissal from the University if during any fall or spring semester their overall UC GPA falls below a 2.00, or their semester GPA is less than 2.00.
Students must achieve a minimum grade point average of 2.00 (C average) in upper division technical courses required for the major curriculum each semester.
A minimum overall grade point average of 2.00, and a minimum 2.00 grade point average in upper division technical course work required for the major is needed to earn a Bachelor of Science in Engineering.
Unit Requirements
To earn a Bachelor of Science in Engineering, students must complete at least 120 semester units of courses subject to certain guidelines:
Completion of the requirements of one engineering major program of study.
A maximum of 16 units of special studies coursework (courses numbered 97, 98, 99, 197, 198, or 199) is allowed towards B.S. degree, and no more than 4 units in any single term can be counteds.
A maximum of 4 units of physical education from any school attended will count towards the 120 units.
Passed (P) grades may account for no more than one third of the total units completed at UC Berkeley, Fall Program for Freshmen (FPF), UC Education Abroad Program (UCEAP), or UC Berkeley Washington Program (UCDC) toward the 120 overall minimum unit requirement. Transfer credit is not factored into the limit. This includes transfer units from outside of the UC system, other UC campuses, credit-bearing exams, as well as UC Berkeley Extension XB units.
Normal Progress
Students in the College of Engineering must enroll in a full-time program and make normal progress each semester toward the bachelor's degree. The continued enrollment of students who fail to achieve minimum academic progress shall be subject to the approval of the dean. (Note: Students with official accommodations established by the Disabled Students' Program, with health or family issues, or with other reasons deemed appropriate by the dean may petition for an exception to normal progress rules.)
All students who will enter the University of California as freshmen must demonstrate their command of the English language by fulfilling the Entry Level Writing Requirement. Satisfaction of this requirement is also a prerequisite to enrollment in all Reading and Composition courses at UC Berkeley.
The American History and Institutions requirements are based on the principle that a U.S. resident graduated from an American university should have an understanding of the history and governmental institutions of the United States.
The American Cultures requirement is a Berkeley campus requirement, one that all undergraduate students at Berkeley need to pass in order to graduate. You satisfy the requirement by passing, with a grade not lower than C- or P, an American Cultures course. You may take an American Cultures course any time during your undergraduate career at Berkeley. The requirement was instituted in 1991 to introduce students to the diverse cultures of the United States through a comparative framework. Courses are offered in more than fifty departments in many different disciplines at both the lower and upper division level.
The American Cultures requirement and courses constitute an approach that responds directly to the problem encountered in numerous disciplines of how better to present the diversity of American experience to the diversity of American students whom we now educate.
Faculty members from many departments teach American Cultures courses, but all courses have a common framework. The courses focus on themes or issues in United States history, society, or culture; address theoretical or analytical issues relevant to understanding race, culture, and ethnicity in American society; take substantial account of groups drawn from at least three of the following: African Americans, indigenous peoples of the United States, Asian Americans, Chicano/Latino Americans, and European Americans; and are integrative and comparative in that students study each group in the larger context of American society, history, or culture.
This is not an ethnic studies requirement, nor a Third World cultures requirement, nor an adjusted Western civilization requirement. These courses focus upon how the diversity of America's constituent cultural traditions have shaped and continue to shape American identity and experience.
Visit the Class Schedule or the American Cultures website for the specific American Cultures courses offered each semester. For a complete list of approved American Cultures courses at UC Berkeley and California Community Colleges, please see the American Cultures Subcommittee’s website. See your academic adviser if you have questions about your responsibility to satisfy the American Cultures breadth requirement.
Plan of Study
For more detailed information regarding the courses listed below (e.g., elective information, GPA requirements, etc.,), please see the College Requirements and Major Requirements tabs.
The Humanities/Social Sciences (H/SS) requirement includes two approved reading and composition courses and four additional approved courses, with which a number of specific conditions must be satisfied. The first half (R&C Part A) must be completed by the end of the freshman year; the second half (R&C Part B) must be completed by no later than the end of the sophomore year. The remaining courses may be taken at any time during the program. See engineering.berkeley.edu/hss for complete details and a list of approved courses.
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Students must acquire fluent programming skills as demonstrated by completion of coursework in a high-level language such as Python, C, C++ or Java. This requirement may be completed by taking COMPSCI C8 or COMPSCI 61A or equivalent. The CS 9xx series self-paced courses are intended for those already skilled as programmers in a high-level language to learn a second language and thus are not appropriate for meeting this requirement. Junior transfer admits who have completed a programming course of 3 units or more in Python, C, C++, or Java have fulfilled this requirement.
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IND ENG 172 is an alternative course for STAT 134 or STAT 140. In semesters when IND ENG 172 is offered, it is recommended that students take IND ENG 172. Students will receive credit for only one of these courses.
Free electives can be any technical or non-technical course, a course of your interest offered by any department. There are no restrictions.
Student Learning Goals
Learning Goals for the Major
The IEOR Department has five general objectives for its Bachelor of Science (BS) degree program. It aims for BS degree graduates to become highly skilled in:
Quantitative modeling and analysis of a broad array of systems-level decision problems concerned with economic efficiency, productivity, and quality.
Development and creative use of analytical and computational methods for solving these problems.
Collection of and analysis of data, and the use of database and decision-support tools.
Comprehension and analysis of uncertainty.
In addition, the department expects their graduates to obtain the broader skills, background, and knowledge necessary to be an effective professional in a rapidly changing global economy.
All Berkeley engineering graduates acquire the following skills and knowledge:
Ability to apply knowledge of mathematics, science, and engineering.
Ability to design and conduct experiments, analyze, and interpret data.
Ability to design a system, component, or process to meet desired needs.
Ability to function on multi-disciplinary teams.
Ability to identify, formulate, and solve engineering problems.
Understanding of professional and ethical responsibility.
Ability to communicate effectively.
Understand impact of engineering solutions in a global and societal context.
Recognition of need for and ability to engage in life-long learning.
Knowledge of contemporary issues.
Ability to use techniques, skills, and modern engineering tools for engineering practice.
More specific outcomes of the IEOR BS degree program are as follows:
Identify opportunities for improvement in practical settings.
Document process, material and information flows.
Collect and structure data to support decision-making.
Define appropriate goals and constraints for decision-making.
Formulate mathematical optimization models for decision-making.
Model the probabilistic aspects of a system.
Validate modeling assumptions and model implications.
Explore model sensitivity to assumptions and parameters.
Apply appropriate solution techniques for optimization problems.
Perform statistical analysis to identify patterns, test hypotheses, and make estimates or forecasts.
Utilize decision support (e.g., optimization, simulation, decision analysis) software.
Use business software (e.g., Excel) and the Internet to analyze and solve problems.
Utilize quantitative tools for specific applications (e.g., inventory, scheduling, supply chain design, quality control).
Adapt or modify known solution approaches for new problem settings.
Consider humans and organizations in designing systems.
Communicate orally and in writing.
Work in a team.
Understand professional and ethical responsibilities.
Recognize need for and possess ability to engage in lifelong learning.
Advising
Advising Values
Student Success: Above all, the department is dedicated to maximizing student potential and to helping students succeed in their University experiences. The department encourages students to explore their minds and their hearts, challenges them to do their best work, and helps them realize their talents and passions and achieve their goals.
Equity & Inclusion: The department is committed to creating an inclusive environment in which any individual or group can be and feel welcomed, respected, supported and valued. It aspires to provide fair treatment, access, opportunity, and advancement for all students and to identify and eliminate barriers that prevent the full participation of all.
Health & Well-Being: The department collaborates with campus partners to keep the IEOR community healthy by helping students balance the physical, intellectual, emotional, social, occupational, spiritual, and environmental aspects of life.
Advising Excellence: In all that it does, the department strives to deliver personalized advising services of the highest quality. It seeks to continuously educate itself on developments in the field and to evaluate, improve, and streamline its services to support students in obtaining the best education and experience possible.
Advising Staff and Advising Hours
Academic Advising College of Engineering Undergraduate Adviser
Dawn Strough dstrough@berkeley.edu
230 Bechtel Engineering Center
510-642-7594
Academic Opportunities
Student Groups and Organizations
The Industrial Engineering and Operations Research (IEOR) Department is very proud that its students not only excel in academics but also in social organization. The department hosts three professional student organizations that engage in activities such as advising, recruiting and graduate schools information, alumni relations, academic conference organization, and social events. For information regarding student groups, please see the following websites:
The College of Engineering encourages all undergraduates in the college to study abroad. Whether students are interested in fulfilling general education requirements, taking courses related to their major/career, or simply living and studying in a country that is of interest to them, the department will work with students to make it happen. For information about study abroad programs, please see the Berkeley Study Abroad website.
Career Services
The Career Center offers personalized career counseling and a wide variety of professional development workshops on topics such as networking as a job search strategy, getting results from the internet job search, internship search and success strategies, and applying for graduate school. For further information, please see the Career Services website.
Courses
Industrial Engineering and Operations Research
Terms offered: Fall 2017, Fall 2016, Fall 2015
The Berkeley Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Berkeley Seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Freshman Seminars: Read More [+]
Objectives & Outcomes
Course Objectives: Provide an introduction to the field of Industrial Engineering and Operations Research through a series of lectures.
Student Learning Outcomes: Learn more about Industrial Engineering and Operations Research.
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of seminar per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.
Terms offered: Fall 2016
This Freshman-level Introductory course will provide an intuitive overview of the fundamental problems addressed and methods in the fields of Industrial Engineering and Operations Research including Constrained Optimization, Human Factors, Data Analytics, Queues and Chains, and Linear Programming. The course will focus on two-dimensional, i.e., bivariate, examples where the problems and methods are amenable to visualization and geometric intuition. The course will discuss applications such as dieting, scheduling, and transportation. This course will not require pre-requisites and will present the core concepts in a self-contained manner that is accessible to Freshmen to provide the foundation for future coursework. A Bivariate Introduction to IE and OR: Read More [+]
Objectives & Outcomes
Course Objectives: •
Provide a broad survey of the important topics in IE and OR, and develop intuition about problems, algorithms, and abstractions using bivariate examples (2D).
•
Describe different mathematical abstractions used in IEOR (e.g., graphs, queues, Markov chains), and how to use these abstractions to model real-world problems.
•
Introduce students to the data analysis process including: developing a hypothesis, acquiring data, processing the data, testing the hypothesis, and presenting results.
•
Provide students with concrete examples of how the mathematical tools from the class apply to real problems such as dieting, scheduling, and transportation.
Rules & Requirements
Credit Restrictions: Course restricted to Freshman students.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
This lecture series serves as an entry point for undergraduate and graduate curriculum sequences in entrepreneurship and innovation. The series, established in 2005, is named in honor of A. Richard Newton, a visionary technology industry leader and late dean of the University of California Berkeley College of Engineering. The course features a selection of high-level industry speakers who share their insights on industry developments, leadership, and innovation based on their careers. A. Richard Newton Lecture Series: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of colloquium per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Alternative to final exam.
Terms offered: Fall 2020, Fall 2019, Fall 2018
Design and implementation of databases, with an emphasis on industrial and commercial applications. Relational algebra, SQL, normalization. Students work in teams with local companies on a database design project. WWW design and queries. Industrial and Commercial Data Systems: Read More [+]
Rules & Requirements
Prerequisites: Upper division standing
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Prior to 2007
Economic analysis for engineering decision making: Capital flows, effect of time and interest rate. Different methods of evaluation of alternatives. Minimum-cost life and replacement analysis. Depreciation and taxes. Uncertainty; preference under risk; decision analysis. Capital sources and their effects. Economic studies. Formerly Engineering 120. Principles of Engineering Economics: Read More [+]
Rules & Requirements
Credit Restrictions: Students will receive 2 units for 120 after taking Civil Engineering 167. Students will not receive credit after taking Engineering 120.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Summer: 8 weeks - 4 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Fall 2019, Fall 2018
Analytical techniques for the improvement of manufacturing performance along the dimensions of productivity, quality, customer service, and throughput. Techniques for yield analysis, process control, inspection sampling, equipment efficiency analysis, cycle time reduction, and on-time delivery improvement. Applications on semiconductor manufacturing or other industrial settings. Methods of Manufacturing Improvement: Read More [+]
Terms offered: Fall 2020, Spring 2020, Spring 2019
This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks. Applied Data Science with Venture Applications: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Students will be able to design and build data sample application systems that can interpret and use data for a wide range of real life applications across many disciplines and industries;
implement these concepts within applications with modern open source CS tools.
understand relevant mathematical concepts that are used in systems that process data;
Rules & Requirements
Prerequisites: Prerequisites include the ability to write code in Python, and a probability or statistics course. This course is ideal for students who have taken COMPSCI/INFO/STAT C8
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Fall 2019, Fall 2018, Fall 2017
This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning. Introduction to Machine Learning and Data Analytics: Read More [+]
Objectives & Outcomes
Course Objectives: 1.
To expose students to a variety of statistical learning methods, all of which are relevant in useful in wide range of disciplines and applications. 2.
To carefully present the statistical and computational assumptions, trade-offs, and intuition underlying each method discussed so that students will be trained to determine which techniques are most appropriate for a given problem. 3.
Through a series of real-world examples, students will learn to identify opportunities to leverage the capabilities of data analytics and will see how data analytics can provide a competitive edge for companies. 4.
To train students in how to actually apply each method that is discussed in class, through a series of labs and programming exercises. 5.
For students to gain some project-based practical data science experience, which involves identifying a relevant problem to be solved or question to be answered, gathering and cleaning data, and applying analytical techniques. 6.
To introduce students to advanced topics that are important to the successful application of machine learning methods in practice, include how methods for prediction are integrated with optimization models and modern optimization techniques for large-scale learning problems.
Rules & Requirements
Prerequisites: IEOR 165 or equivalent course in statistics. Prior exposure to optimization is helpful but not strictly necessary. Some programming experience/literacy is expected
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Fall 2020, Fall 2019, Fall 2018
Quantitative models for operational and tactical decision making in production systems, including production planning, inventory control, forecasting, and scheduling. Production Systems Analysis: Read More [+]
Terms offered: Fall 2020, Fall 2019, Fall 2018
This course is concerned with improving processes and designing facilities for service businesses such as banks, health care organizations, telephone call centers, restaurants, and transportation providers. Major topics in the course include design of service processes, layout and location of service facilities, demand forecasting, demand management, employee scheduling, service quality management, and capacity planning. Service Operations Design and Analysis: Read More [+]
Terms offered: Spring 2020, Spring 2019, Spring 2018
We will focus primarily on both quantitative and qualitative issues which arise in the integrated design and management of the entire logistics network. Models and solution techniques for facility location and logistics network design will be considered. In addition, qualitative issues in distribution network structuring, centralized versus decentralized network control, variability in the supply chain, strategic partnerships, and product design for logistics will be considered through discussions and cases. Logistics Network Design and Supply Chain Management: Read More [+]
Terms offered: Fall 2020, Fall 2019, Fall 2018
This course introduces unconstrained and constrained optimization with continuous and discrete domains. Convex sets and convex functions; local optimality; KKT conditions; Lagrangian duality; steepest descent and Newton's method. Modeling with integer variables; branch-and-bound method; cutting planes. Models on production/inventory planning, logistics, portfolio optimization, factor modeling, classification with support vector machines. Nonlinear and Discrete Optimization: Read More [+]
Terms offered: Fall 2020, Spring 2020, Fall 2019
This course addresses modeling and algorithms for optimization of linear constrained optimization problems. The simplex method; theorems of duality; complementary slackness. Applications in production planning and resource allocation. Graph and network problems as linear programs with integer solutions. Algorithms for selected network flow problems. Transportation and logistics problems. Dynamic programming and its role in applications to shortest paths, project management and equipment replacement. Linear Programming and Network Flows: Read More [+]
Terms offered: Not yet offered
Designed for students from any science/engineering major, this upper-division course will introduce students to optimization models, and train them to use software tools to model and solve optimization problems. The main goal is to develop proficiency in common optimization modeling languages, and learn how to integrate them with underlying optimization solvers. Students will work primarily on modeling exercises, which will develop confidence in modeling and solve optimization methods using software packages, and will require some programming.
Review of linear and nonlinear optimization models, including optimization problems with discrete decision variables. Applications to practical problems from engineering and data science. Introduction to Optimization Modeling: Read More [+]
Objectives & Outcomes
Course Objectives: •
To introduce students to the core concepts of optimization
•
To train them in the art and science of using software tools to model and solve optimization problems.
Rules & Requirements
Prerequisites: No prerequisites except some Python programming skills, which can be met by COMPSCI C8 (or any other Python-based course)
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Spring 2020, Spring 2019, Spring 2018
This course will introduce students to basic statistical techniques such as parameter estimation, hypothesis testing, regression analysis, analysis of variance. Applications in forecasting and quality control.
Terms offered: Fall 2019, Spring 2018, Spring 2017
Introductory course on the theory and applications of decision analysis. Elective course that provides a systematic evaluation of decision-making problems under uncertainty. Emphasis on the formulation, analysis, and use of decision-making techniques in engineering, operations research and systems analysis. Includes formulation of risk problems and probabilistic risk assessments. Graphical methods and computer software using event trees, decision trees, and influence diagrams that focus on model design. Decision Analytics: Read More [+]
Terms offered: Spring 2020, Spring 2019
This course addresses modeling and algorithms for integer programming problems, which are constrained optimization problems with integer-valued variables. Flexibility of integer optimization formulations; if-then constraints, fixed-costs, etc. Branch and Bound; Cutting plane methods; polyhedral theory. Applications in production planning, resource allocation, power generation, network design. Alternate formulations for integer optimization: strength of Linear Programming relaxations. Algorithms for integer optimization problems. Specialized strategies by integer programming solvers. Integer Optimization: Read More [+]
Objectives & Outcomes
Course Objectives: •
Enable the students to recognize when problems can be modeled as integer optimization problems. •
Familiarize students in leading methodologies for solving integer optimization problems, and techniques in these methodologies. •
To acquire skills in the best modeling approach that is suitable to the practical problem at hand. •
To train students in modeling of integer optimization problems; •
To train the students in the selection of appropriate techniques to be used for integer optimization problems.
Rules & Requirements
Prerequisites:MATH 53, MATH 54, and background in Python and programming
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Spring 2020, Spring 2019, Spring 2018
This course surveys topics related to the design of products and interfaces ranging from alarm clocks, cell phones, and dashboards to logos, presentations, and web sites. Design of such systems requires familiarity with human factors and ergonomics, including the physics and perception of color, sound, and touch, as well as familiarity with case studies and contemporary practices in interface design and usability testing. Students will solve a series of design problems individually and in teams. Industrial Design and Human Factors: Read More [+]
Rules & Requirements
Prerequisites: Upper division standing
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
This course explores key management and leadership concepts relevant to the high-technology world. Topics include the firm's key operations, strategic issues, and managerial leadership including personal leadership and talent management. This course prepares technical and business minded students for careers focused on professional and management track careers in high technology. Students undertake intensive study of actual business situations through rigorous case-study analysis. Technology Firm Leadership: Read More [+]
Rules & Requirements
Prerequisites: Upper division standing
Credit Restrictions: Students will receive no credit for Ind Eng 171 after taking UGBA 105.
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Summer: 8 weeks - 6 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Fall 2019, Fall 2018
This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. To complement the theory, the course also covers the basics of stochastic simulation. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.
Course Objectives: Students will learn how to model random phenomena and learn about a variety of areas where it is important to estimate the likelihood of uncertain events. Students will also learn how to use computer simulation to replicate and analyze these events.
Rules & Requirements
Prerequisites: Students should have a solid knowledge of calculus, including multiple variable integration, such as MATH 1A and MATH 1B or MATH 16A and MATH 16B, as well as programming experience in Matlab or Python
Terms offered: Spring 2020, Spring 2019, Spring 2018
This is an introductory course in stochastic models. It builds upon a basic course in probability theory and extends the concept of a single random variable into collections of random variables known as stochastic processes. The course focuses on discrete-time Markov chains, Poisson process, continuous-time Markov chains, and renewal theory. It also discusses applications to queueing theory, risk analysis and reliability theory. Along with the theory, the course covers stochastic simulation techniques that will allow students to go beyond the models and applications discussed in the course. Introduction to Stochastic Processes: Read More [+]
Objectives & Outcomes
Course Objectives: Students will learn how to model random phenomena that evolves over time, as well as the simulation techniques that enable the replication of such problems using a computer. By discussing various applications in science and engineering, students will be able to model many real world problems where uncertainty plays an important role.
Rules & Requirements
Prerequisites: Students should have taken a probability course, such as STAT 134 or IND ENG 172, and should have programming experience in Matlab or Python
Credit Restrictions: Students will receive no credit for Ind Eng 173 after taking Ind Eng 161.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2020, Spring 2020, Spring 2019
Introductory course on design, programming, and statistical analysis of simulation methods and tools for enterprise-scale systems such as traffic and computer networks, health-care and financial systems, and factories. Topics include the types of problems that can be solved by such methods. Programming material includes the theory behind random variable generation for a variety of common variables. Advanced techniques such as variance reduction, simulation optimization, or meta-modeling are considered. Student teams implement an enterprise-scale simulation in a semester-length design project. Simulation for Enterprise-Scale Systems: Read More [+]
Objectives & Outcomes
Course Objectives: •
Exposure students to state-of-art advanced simulation techniques. •
Note: the course is a mixture of modeling art, analytical science, and computational technology. •
Have students communicate their ideas and solutions effectively in written reports. •
Insure students become familiar with the fundamental similarities and differences among simulation software packages. •
Introduce students to modern techniques for developing computer simulations of stochastic discrete-event models and experimenting with such models to better design and operate dynamic systems. •
Introduce the different technologies used to develop simulation models and simulator products in order to become critical consumers of simulation study results. •
Teach strengths and weaknesses of different approaches for a foundation for selecting methodologies. •
Teach students how to model random processes and experiment with simulated systems.
Credit Restrictions: Students will receive no credit for IND ENG 174 after completing IND ENG 131. A deficient grade in IND ENG 174 may be removed by taking IND ENG 131.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2020, Fall 2019, Spring 2019
Application of systems analysis and industrial engineering to the analysis, planning, and/or design of industrial, service, and government systems. Consideration of technical and economic aspects of equipment and process design. Students work in teams under faculty supervision. Topics vary yearly. Senior Project: Read More [+]
Rules & Requirements
Prerequisites: 160, 162, 165, 173, Engineering 120, and three other Industrial Engineering and Operations Research electives
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 6 hours of fieldwork per week
Summer: 10 weeks - 3 hours of lecture and 9 hours of fieldwork per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
This course is meant for students in engineering and other disciplines who seek a challenging, interactive, team-based, and hands-on learning experience in entrepreneurship and technology. In this highly experiential course, students work in simulated start-up teams to create products or start-up ideas to address a broadly-defined need of an industry partner or social challenge. Challenge Lab: Read More [+]
Objectives & Outcomes
Course Objectives: 1)
To catalyze learning through experiential entrepreneurship 2)
To help students understand the entrepreneurial context, and how it can create better outcomes. 3)
To help students identify the best role for themselves within an entrepreneurial organization.
Student Learning Outcomes: 1)
Gain experience with effectively refining ideas and pivoting based on feedback and external factors.
2)
Gain experience building effective teams to develop and execute an idea
3)
Become comfortable with failure and how to learn from failure.
4)
Become adept at succinctly communicating ideas in terms of value proposition and business viability.
Rules & Requirements
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 4 hours of seminar per week
Summer: 6 weeks - 10 hours of seminar per week 8 weeks - 7.5 hours of seminar per week 10 weeks - 6 hours of seminar per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Instructors: Goldberg, Sidhu, Wroblewki, IEOR / CET Instructors
Terms offered: Fall 2019, Spring 2019, Spring 2018
Too often we are enamored in our brilliant ideas, we skip the most important part: building products consumers will want and use. Precious time and effort is wasted on engineering perfect products only to launch to no users. This course teaches product management skills such as attributes of great product managers, reducing risk and cost while accelerating time to market, product life cycle, stakeholder management and effective development processes. Product Management: Read More [+]
Objectives & Outcomes
Course Objectives: •
Students will experience a live development of a product within the context of a product development process. •
Students will learn common methods used in product management •
Students will understand the difference between engineering design and product development as a process commonly used in new venture environments.
Student Learning Outcomes: •
Students will actually develop a real world functioning product, to be described as Minimum Viable.
•
Students will be able to manage a product development process that leads to a product that is technically feasible as well as desired by customers.
•
Students will gain experience needed to work as product managers in real life environments.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of seminar per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2011
This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company, product, or service. Over the duration of this course, students will examines case studies of early, mid-stage, and large-scale enterprises as they seek to start a new venture, introduce a new product or service, or capitalize on global economic trends to enhance their existing business. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion. Cases will include both U.S. companies seeking to enter emerging markets and emerging market companies looking to expand within their own nations or into markets in developed nations. The course is focused around intensive study of actual business situations through rigorous case-study analysis. Cases in Global Innovation: Read More [+]
Rules & Requirements
Prerequisites: Junior or Senior standing
Hours & Format
Fall and/or spring: 8 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Prior to 2007
This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company product or service, with a focus on China. Over the duration of this course, students will examine case studies of foreign companies seeking to start a new venture, introduce a new product or service to the China market, or domestic Chinese companies seeking to adapt a U.S. or western business model to the China market. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion and the particularities of the China market and their contrast with the U.S. market. The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. Cases in Global Innovation: China: Read More [+]
Rules & Requirements
Prerequisites: Junior or senior standing. Recommended, but not required to be taken after or along with Engineering 198
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Prior to 2007
This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in conducting business, globalizing a company product or service, or investing in South Asia. Over the duration of this course, students will examine case studies of foreign companies seeking to start a new venture, introduce a new product or service to the South Asian market, or South Asian companies seeking to adapt a U.S or western business model. The course will put this into the larger context of the political, economic, and social climate in several South Asian countries and explore the constraints to doing business, as well as the policy changes that have allowed for a more conducive business environment. Cases in Global Innovation: South Asia: Read More [+]
Rules & Requirements
Prerequisites: Junior or senior standing. Recommended but not required to be taken after or along with Engineering 198
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Spring 2020, Fall 2019, Spring 2019
This course explores key entrepreneurial concepts relevant to the high-technology world. Topics include the entrepreneurial perspective, start-up strategies, business idea evaluation, business plan writing, introduction to entrepreneurial finance and venture capital, managing growth, and delivering innovative products. This course prepares technical and business minded students for careers focused on entrepreneurship, intrapreneurship, and high technology. Students undertake intensive study of actual business situations through rigorous case-study analysis. This course can not be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Technology Entrepreneurship: Read More [+]
Rules & Requirements
Prerequisites: Junior or senior standing
Credit Restrictions: Students will receive no credit for 191 after taking 190A prior to fall 2009.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Final exam not required.
Terms offered: Fall 2020, Spring 2020, Fall 2019
This course offers the opportunity to understand the Berkeley Method of Entrepreneurship (BME) in an intensive format. The BME curriculum conveys the latest approaches for training global technology entrepreneurs. This method leverages insights on strategy, tactics, culture, and psychology with an accompanying entrepreneurial infrastructure. The curriculum is structured to provide an optimal global entrepreneurship experience from real life experiences. Berkeley Method of Entrepreneurship Bootcamp: Read More [+]
Objectives & Outcomes
Course Objectives: * To understand and make use of the value of diversity in idea generation and new venture creation. Student should become aware of the infrastructure available through UC Berkeley that an support them in developing new ventures. To understand common tactics in starting new ventures including a lean learning cycle. To understand the mindset of an entrepreneur, including the soft skills, behaviors, and psychological factors most likely to be needed to develop a new venture.
Student Learning Outcomes: Students should be able to consider a greater number of ideas for global entrepreneurship by observing the effect of background diversity in the class.
Students should be able to follow a process of idea generation, rapid prototyping / venture story development, attraction of stakeholders, data collection, and hypothesis testing and regeneration.
Students should become aware of the mindset and behaviour required for entreprenurship and be able to reinforce some of these behavious (eg rejection tolerance, comfort with failing or being wrong, inductive learning, venture story telling/communication abilities) through excercizes in the program.
Hours & Format
Fall and/or spring: 1 weeks - 30 hours of lecture and 20 hours of discussion per week
Summer: 3 weeks - 30 hours of lecture and 20 hours of discussion per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Fall 2020, Spring 2020, Fall 2019
This lecture series serves as an entry point for undergraduate and graduate curriculum sequences in entrepreneurship and innovation. The series, established in 2005, is named in honor of A. Richard Newton, a visionary technology industry leader and late dean of the University of California Berkeley College of Engineering. The course features a selection of high-level industry speakers who share their insights on industry developments, leadership, and innovation based on their careers. A. Richard Newton Lecture Series: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of colloquium per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Alternative to final exam.
Terms offered: Fall 2020, Fall 2019, Summer 2019 10 Week Session
Students work on a field project under the supervision of a faculty member. Course does not satisfy unit or residence requirements for bachelor's degree. Undergraduate Field Research in Industrial Engineering: Read More [+]
Rules & Requirements
Prerequisites: Completion of two semesters of coursework
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-12 hours of fieldwork per week
Summer: 6 weeks - 2.5-30 hours of fieldwork per week 8 weeks - 1.5-22.5 hours of fieldwork per week 10 weeks - 1.5-18 hours of fieldwork per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
Prerequisites: Consent of instructor and major adviser
Credit Restrictions: Course may be repeated for a maximum of four units per semester.
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of independent study per week
Summer: 6 weeks - 2.5-10 hours of independent study per week 8 weeks - 2-7.5 hours of independent study per week 10 weeks - 1.5-6 hours of independent study per week
Additional Details
Subject/Course Level: Industrial Engin and Oper Research/Undergraduate
Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.
Ilan Adler, Professor. Financial engineering, optimization theory, combinatorial probability models. Research Profile
Anil Jayanti Aswani, Assistant Professor.
Alper Atamturk, Professor. Logistics, integer programming, computational optimization, robust optimization. Research Profile
Laurent El Ghaoui, Professor. Decision-making under uncertainty, convex optimization, robust solutions, semidefinite programming, exhaustive simulation. Research Profile
Lee Fleming, Professor. Invention, innovation, patents, big data, leadership. Research Profile
Ken Goldberg, Professor. Robotics, art, social media, new media, automation. Research Profile
Paul Grigas, Assistant Professor. Large-scale convex optimization, statistical machine learning, and data-driven decision making. Research Profile
Xin Guo, Professor. Financial engineering, industrial engineering and operations, stochastic processes and applications, stochastic control, semi-martingale and filteration expansions, credit risk, (ir)reversible investment. Research Profile
Dorit S. Hochbaum, Professor. Data mining, integer programming, discrete optimization, network flow techniques, clustering, image segmentation, machine vision, pattern recognition. Research Profile
Philip M. Kaminsky, Professor. Biotechnology, logistics, distribution, algorithms, planning, optimization, control, manufacturing, semiconductors, scheduling, biomanufacturing, probabilistic methods, production scheduling, supply chain management, operations management, logistic. Research Profile
Javad Lavaei, Associate Professor. Control theory, optimization theory, power systems, and data science. Research Profile
Robert C. Leachman, Professor. Logistics, manufacturing, semiconductors, scheduling, supply chain systems, dynamic production models, production planning and scheduling. Research Profile
Shmuel S. Oren, Professor Emeritus. Economics, algorithms, financial engineering, risk management, planning, optimization, operation of electric power systems, market based coordination of network systems, trading instruments. Research Profile
Rhonda L. Righter, Professor. Modeling, optimization, stochastic systems, systems with uncertainty. Research Profile
Lee W. Schruben, Professor. Health care systems, simulation, optimization of simulation system response, foundations of simulation modeling, supply chains, experimental designs, biopharmaceuticals, Production. Research Profile
Zuo-Jun Shen, Professor. Logistics, supply chain design and management, inventory management, auction mechanism design. Research Profile
Ikhlaq Sidhu, Adjunct Professor. Technology management, industrial engineering and operations, technology commerialization, interdisciplinary engineering. Research Profile
Candace Yano, Professor. Inventory control, production planning, distribution systems planning, integrated production-quality models, integrated manufacturing-marketing models. Research Profile
Zeyu Zheng, Assistant Professor.
Lecturers
David Law, Lecturer.
Mehdi Maghsoodnia, Lecturer.
Kenneth Sandy, Lecturer.
Ken Singer, Lecturer.
Naeem Zafar, Lecturer.
Emeritus Faculty
Richard E. Barlow, Professor Emeritus. Industrial engineering and operations, reliability theory, statistical data analysis, Bayesian probability modeling. Research Profile
Stuart E. Dreyfus, Professor Emeritus. Neural networks, dynamic programming, limits of operations research modeling, cognitive ergonomics. Research Profile
C. Roger Glassey, Professor Emeritus. Simulation of manufacturing systems, production planning and scheduling, mathematical optimization. Research Profile
Robert M. Oliver, Professor Emeritus. Risk management, operations research, industrial engineering, prediction of rare events, default and fraud detection, credit risk scoring, analysis tools, computer software, acquisition and negotiation strategies. Research Profile
Sheldon M. Ross, Professor Emeritus. Financial engineering, simulations, stochastics, statistical analysis. Research Profile
J. George Shanthikumar, Professor Emeritus. Scheduling, production system modelling and analysis, queueing theory and applications, reliability and probability theory, sequencing, simulation methodology, stochastic processes and modelling. Research Profile
Ronald W. Wolff, Professor Emeritus. Stochastic processes, queueing theory, queuing network, transmission systems. Research Profile
Contact Information
Department of Industrial Engineering and Operations Research
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