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, Principles of Engineering Economics: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Supply Chain and Logistics Management: Read More [+], Supply Chain and Logistics Management: Read Less [-], Terms offered: Spring 2014, Fall 2011, Fall 2009 On the theoretical front, supply chain analysis inspires new research ventures that blend operations research, game theory, and microeconomics. Sensitivity analysis, parametric programming, convergence (theoretical and practical). Mathematical Programming II: Read More [+], Mathematical Programming II: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Development of analytical tools for improving efficiency, customer service, and profitability of production environments. Relationship to theory of production, inventory theory and hierarchical organization of production management. Topics vary yearly. Students develop research designs and present each week and formally for their final. Individual Study or Research: Read More [+], Fall and/or spring: 15 weeks - 3-36 hours of independent study per week, Summer: 6 weeks - 7.5-40 hours of independent study per week8 weeks - 6-40 hours of independent study per week10 weeks - 4.5-40 hours of independent study per week. These topics include complexity analysis of algorithms and its drawbacks; solving a system of linear integer equations and inequalities; strongly polynomial algorithms, network flow problems (including matching and branching); polyhedral optimization; branch and bound and lagrangean relaxation. On the other hand, the Master of Analytics focuses on . Students will work on group projects along with Decision Analytics: Read More [+], Terms offered: Spring 2022, Spring 2021, Fall 2020 Includes formulation of risk problems and probabilistic risk assessments. Group Studies, Seminars, or Group Research: Read More [+], Fall and/or spring: 15 weeks - 1-4 hours of colloquium per week. 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. Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. The actual subjects covered may include: Convex analysis, duality theory, complementary pivot theory, fixed point theory, optimization by vector space methods, advanced topics in nonlinear algorithms, complexity of mathematical programming algorithms (including linear programming). One of the grand challenges of this century is the modernization of electrical power networks. Provide a broad survey of the important topics in IE and OR, and develop intuition about problems, algorithms, and abstractions using bivariate examples (2D). This course is targeted at understanding RM problems in the booming environment of online platforms and marketplaces with applications ranging from online advertising to ride-sharing markets. Standard topics include Girsanov transformation, martingale representation theorem, Feyman-Kac formula, and American and exotic option pricings. Learn more about our facultys research, student activities, alumni game-changers, and how Berkeley IEOR is designing a more efficient world. Portfolio optimization problems will be considered both from a mean-variance and from a utility function point of view. 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. Design and development of effective industrial production planning systems. be used to fulfill any engineering unit or elective requirements. Industrial Engineering and Operations Research 173. Berkeley, CA 94720-1702 (510) 642-7594 ess@berkeley.edu Hours: Monday - Thursday, 8 a.m.-5 p.m. Friday, 10 a.m.-5 p.m. 4141 Etcheverry Hall #1777 (510) 642-5484 ieor.berkeley.edu Degree worksheets: 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019| 2020| 2021| 2022 Advanced seminars in industrial engineering and operations research. Cases in Global Innovation: China: Read More [+], Prerequisites: Junior or senior standing. Advanced techniques such as variance reduction, simulation optimization, or meta-modeling are considered. Course Objectives: 1. Linear Programming and Network Flows: Read More [+], Linear Programming and Network Flows: Read Less [-], Terms offered: Spring 2023 Course Objectives: Provide an introduction to the field of Industrial Engineering and Operations Research through a series of lectures. Service Operations Management: Read More [+], Prerequisites: Students who have not advanced to M.S., M.S./Ph.D., or Ph.D. levels or are not in the Industrial Engineering and Operations Research Department must consult with the instructor before taking this course for credit, Service Operations Management: Read Less [-], Terms offered: Spring 2013, Spring 2012, Spring 2011 Current Readings in Innovation: Read More [+], Prerequisites: Background: upper level standing or graduate student, any school, Fall and/or spring: 15 weeks - 3 hours of seminar per week, Current Readings in Innovation: Read Less [-], Terms offered: Spring 2011, Spring 2010, Spring 2009 Depreciation and taxes. Faculty research in Berkeley IEOR specializes in stochastic processes, optimization, and supply chain management. Three hours of lecture per week. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Graphical methods and computer software using event trees, decision trees, and influence diagrams that focus on model design. Applications in production planning, resource allocation, power generation, network design. After reviewing each concept, we explore implementing it in Python using libraries for math array functions, manipulation of tables, data architectures, natural language, and ML frameworks. Max-flow min-cut theorem. Modeling with integer variables; branch-and-bound method; cutting planes. 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. The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Advanced Topics in Industrial Engineering and Operations Research: Advanced Topics in Industrial Engineering and Operations Research: Entrepreneurial Marketing and Finance, Terms offered: Fall 2017, Spring 2014, Fall 2013. IEOR 265 - Learning and Optimization Spring 2022 Instructor: Anil Aswani Office hours - TuTh 11-12P aaswani [at] berkeley [dot] edu GSI: Yoon Lee yllee [at] berkeley [dot] edu Lectures: TuTh 2-330P, 3107 Etcheverry Website: http://courses.ieor.berkeley.edu/ieor265 Optional Textbooks: Models, algorithms, and analytical techniques for inventory control, production scheduling, production planning, facility location and logistics network design, vehicle routing, and demand forecasting will be discussed. 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. Terms offered: Spring 2019, Spring 2017 Group Studies, Seminars, or Group Research: Read Less [-], Terms offered: Summer 2023 Second 6 Week Session, Fall 2019, Fall 2016 Advanced Mathematical Programming: Read More [+], Advanced Mathematical Programming: Read Less [-], Terms offered: Spring 2016, Spring 2015, Spring 2014 Formerly Engineering 120. 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 second part of the course will discuss the formulation and numerical implementation of learning-based model predictive control (LBMPC), which is a method for robust adaptive optimization that can use machine learning to provide the adaptation. Course does not satisfy unit or residence requirements for bachelor's degree. Exposure students to state-of-art advanced simulation techniques. GSI Ahmad Masad 16amasad[at]berkeley.edu Please include [IEOR 130] at the beginning of your subject, e.g. IEOR improves processes to create a better world. Bounds and approximations. Introduction to Convex Optimization: Read More [+], Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week, Formerly known as: Electrical Engineering C227A/Industrial Engin and Oper Research C227A, Introduction to Convex Optimization: Read Less [-], Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017 Introduction to Production Planning and Logistics Models: Terms offered: Fall 2012, Spring 2005, Spring 2004, Terms offered: Spring 2021, Spring 2014, Spring 2013. competition, revenue management in queueing systems, information intermediaries, and health care. On the practical front, supply chain analysis offers solid foundations for strategic positioning, policy setting, and decision making. Support Berkeleys commitment to excellence and opportunity! Automation Science and Engineering: Read More [+], Fall and/or spring: 15 weeks - 2 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week, Automation Science and Engineering: Read Less [-], Terms offered: Spring 2023, Fall 2022, Spring 2022 Search Courses. The Master of Engineering program in Industrial Engineering & Operations Research is a one year full-time program that combines business-oriented coursework with applications-focused industrial engineering and operations research courses emphasizing Optimization Analytics, Risk Modeling, Simulation, and Data Analysis. Development of dynamic activity analysis models for production planning and scheduling. 30% Notebook with Lecture Notes. The emphasis will be on computational methods such as variants of GARCH, Black-Litterman, conic optimization, Monte Carlo simulation for risk and optimization, factor modeling. Courses Industrial Engineering and Operations Research (IND ENG) Industrial Engineering and Operations Research (IND ENG) Courses Expand all course descriptions [+] IND ENG 24 Freshman Seminars 1 Unit [+] IND ENG 66 A Bivariate Introduction to IE and OR 3 Units [+] IND ENG 98 Supervised Group Study and Research 1 - 3 Units [+] Stochastic simulation ideas will be introduced and used to obtain the risk-neutral geometric Brownian motion values for certain types of Asian, barrier, and lookback options. Applied Data Science with Venture Applications: Read More [+]. Operations Research & Management Science, B.S. 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. Students will undertake computational assignments and a group project. This course introduces unconstrained and constrained optimization with continuous and discrete domains. Credit Restrictions: Students will receive 2 units for 120 after taking Civil Engineering 167. Topics include: preparing a syllabus; public speaking and coping with language barriers; creating effective slides and exams; differing student learning styles; grading; encouraging diversity, equity, and inclusion; ethics; dealing with conflict and misconduct; and other topics relevant to serving as an effective teaching assistant. Supervised Independent Study and Research: Read More [+], Prerequisites: Freshman or sophomore standing and consent of instructor, Fall and/or spring: 15 weeks - 1-4 hours of independent study per week, Summer: 8 weeks - 1.5-7.5 hours of independent study per week10 weeks - 1.5-6 hours of independent study per week, Supervised Independent Study and Research: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 Frontiers in Revenue Management: Read More [+], Prerequisites: IndEng 262A and IndEng 263A (or equivalent coursework) IndEng 264 and IndEng 269 recommended but not required, Frontiers in Revenue Management: Read Less [-], Terms offered: Not yet offered A The reversed chain concept in continuous time Markov chains with applications of queueing theory. Models on production/inventory planning, logistics, portfolio optimization, factor modeling, classification with support vector machines. Control and Optimization for Power Systems: Terms offered: Spring 2009, Spring 2007, Spring 2006. use machine learning to provide the adaptation. 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. Algorithms for integer optimization problems. Study of algorithms for non-linear optimization with emphasis on design considerations and performance evaluation. Risk Modeling, Simulation, and Data Analysis: Read More [+], Prerequisites: Basic notions of probability, statistics, and some programming and spreadsheet analysis experience, Risk Modeling, Simulation, and Data Analysis: Read Less [-], Terms offered: Spring 2023, Fall 2022, Spring 2022 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. When you print this page, you are actually printing everything within the tabs on the page you are on: this may include all the Related Courses and Faculty, in addition to the Requirements or Overview. Optimization Analytics: Read More [+], Prerequisites: Basic analysis and linear algebra, and basic computer skills and experience, Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week, Terms offered: Fall 2022, Fall 2021, Fall 2020 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. Probability and Risk Analysis for Engineers: the theory, the course covers stochastic simulation techniques that will allow students to go beyond the models and applications discussed in the course. The course is focused around intensive study of actual business situations through rigorous case-study analysis. Emphasis on the formulation, analysis, and use of decision-making techniques in engineering, operations research and systems analysis. Markovian queues; product form results. Renewal reward processes with application to inventory, congestion, and replacement models. Students will work in teams on projects and build solutions to Introductory graduate level course, focusing on applications of operations research techniques, e.g., probability, statistics, and optimization, to financial engineering. This course explores key management and leadership concepts relevant to the high-technology world. Learn more about our facultys research, student activities, alumni game-changers, and how Berkeley IEOR is designing a more efficient world. Mathematical Programming I: Read More [+], Mathematical Programming I: Read Less [-], Terms offered: Spring 2023, Spring 2022, Spring 2021 Algorithms for selected network flow problems. Algorithms for integer optimization problems. Models and solution techniques for facility location and logistics network design will be considered. It is applied to a broad range of applications from manufacturing to transporation to healthcare. The course is The goal is for students to develop the experience and intuition to gather and build new datasets and answer substantive questions. Prerequisites: Students should have a solid knowledge of calculus, including multiple variable integration, such as MATH1A and MATH1B or MATH16A and MATH16B, as well as programming experience in Matlab or Python. Lectures and appropriate assignments on fundamental or applied topics of current interest in industrial engineering and operations research. Applications in robust engineering design, statistics, control, finance, data mining, operations research. The course includes laboratory assignments, which consist of hands-on experience. Integrate verbal and visual methods of conveying engineering concepts and practices in the classroom and in discussions.5. Credit Restrictions: Course may be repeated for credit with consent of instructor. 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. Applications in forecasting and quality control. The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. Describe different mathematical abstractions used in IEOR (e.g., graphs, queues, Markov chains), and how to use these abstractions to model real-world problems. All courses are subject to change. data sets. Optimization and Algorithms Machine Learning and Data Science Spring 2018: IEOR 268 - Applied Dynamic Programming. Simulation for Enterprise-Scale Systems: Read Less [-], Terms offered: Spring 2023, Spring 2022, Spring 2021 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. A project course for students interested in applications of operations research and engineering methods. Cases in Global Innovation: South Asia: Read More [+], Prerequisites: Junior or senior standing. 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. These ventures result in an unprecedented amalgamation of prescriptive, descriptive, and predictive models characteristic of each subfield. Uncertainty; preference under risk; decision analysis. Grading Based on: 30% Class Attendance and Participation ; 30% Notebook with Lecture Notes Important models (both centralized and decentralized) for understanding the design, operation, and evaluation of supply chains will be discussed with the goal of developing a holistic understanding of supply chain management. Introductory course on the theory and applications of decision analysis. Prior exposure to optimization is helpful but not strictly necessary.