Introduction to Production Planning and Logistics Models: Read More [+], Prerequisites: 262A and 263A taken concurrently, Introduction to Production Planning and Logistics Models: Read Less [-], Terms offered: Fall 2012, Spring 2005, Spring 2004 Advanced Mathematical Programming: Read More [+], Advanced Mathematical Programming: Read Less [-], Terms offered: Spring 2016, Spring 2015, Spring 2014 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; As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.. This seminar and discussion class aims to survey current and classic research on innovation and help Models and solution techniques for facility location and logistics network design will be considered. A Bivariate Introduction to IE and OR: Read More [+]. The field has made significant strides on both theoretical and practical fronts. Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. Innovations that we will discuss include collaborative forecasting, social media, online procurement, and technologies such as RFID. The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Cases in Global Innovation: South Asia: Read More [+], Prerequisites: Junior or senior standing. Grading Based on: 30% Class Attendance and Participation ; 30% Notebook with Lecture Notes Credit Restrictions: Students will receive 2 units for 120 after taking Civil Engineering 167. Terms offered: Spring 2019, Spring 2017 Minimum-cost life and replacement analysis. May not be used for unit or residence requirements for the doctoral degree. Supervised independent study for lower division students. The course, drawing a mix of humanities and engineering students, will include readings and lectures on 19th and 20th century philosophers with discussions of new technology and team experimental projects. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion. Course Objectives: The simplex method and its variants. Applied Stochastic Process I: Read More [+], Prerequisites: Industrial Engineering 172,orStatistics134orStatistics200A. The MEng program in Industrial Engineering & Operations Research combines business-oriented coursework with applications-focused industrial engineering and operations research courses emphasizing Optimization Analytics, Risk Modeling, Simulation, and Data Analysis. With the growing complexity of providing healthcare, it is increasingly important to design and manage health systems using engineering and analytics perspectives. Students will work in teams on projects and build solutions to Students will solve a series of design problems individually and in teams. Advanced topics in information management, focusing on design of relational databases, querying, and normalization. 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 Work conservation; priorities. On the practical front, supply chain analysis offers solid foundations for strategic positioning, policy setting, and decision making. Portfolio optimization problems will be considered both from a mean-variance and from a utility function point of view. On the theoretical front, supply chain analysis inspires new research ventures that blend operations research, game theory, and microeconomics. Control and Optimization for Power Systems. You will learn techniques to accelerate product success and avoid common mistakes. 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. This course will cover topics related to healthcare analytics, including: optimizing chronic disease management, designing matching markets for health systems, developing predictive analytics models, and managing resource utilization. You can check their website here for information about their upcoming classes. Optimality conditions for non linear optimization problems. Terms offered: Fall 2017, Fall 2016, Fall 2015 Brownian Motion. GSI Ahmad Masad 16amasad[at]berkeley.edu Please include [IEOR 130] at the beginning of your subject, e.g. Faculty research in Berkeley IEOR specializes in stochastic processes, optimization, and supply chain management. The last part of the course will deal with inverse decision-making problems, which are problems where an agent's decisions are observed and used to infer properties about the agent. Dynamic programming formulation of deterministic decision process problems, analytical and computational methods of solution, application to problems of equipment replacement, resource allocation, scheduling, search and routing. Analysis of the capacity and efficiency of production systems. 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 Student Learning Outcomes: LEARNING GOALS All courses are subject to change. The course is focused around intensive study of actual business situations through rigorous case-study analysis. https://ieor.berkeley.edu/wp-content/uploads/2021/10/iise_EDIT_2_captions.mp4, Meet One of UC Berkeleys Oldest Living Alumni, Dr. Ernst S. Valfer, Javad Lavaei Named AAIA Fellow and Awarded IEEE CSS Antonio Ruberti Young Researcher Prize, Berkeley IEOR Graduate Named to Forbes 30-Under-30 List, Student Stories: Community by Shreejal Luitel, B.A. Algorithms for integer optimization problems. The main goal is to develop proficiency in common optimization modeling languages, and learn how to integrate them with underlying optimization solvers. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. Students taking Ind Eng 242 cannot receive credit for Ind Eng 142. Operations Research and Management Science Honors Thesis. Topics covered are from a broad range that includes demand modeling, inventory management, facility location as well as process flexibility, contracting, and auctions. [IEOR 130] Questions for Homework 1 Individual investigation of advanced industrial engineering problems. In this graduate course, we focus on the systematic design of databases and interfaces for commercial and industrial applications. with risk-neutral pricing in continuous time models. Control and Optimization for Power Systems: Read More [+]. Search Courses. Prerequisites: MATH53, MATH54, and background in Python and programming, Terms offered: Spring 2023, Spring 2022, Spring 2021 Industrial Engineering & Operations Research, Management, Entrepreneurship & Technology, Ph.D. Industrial Engineering & Operations Research. 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. Topics include the types of problems that can be solved by such methods. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. doctoral students formulate their research designs. Renewal reward processes with application to inventory, congestion, and replacement models. recommendations. The course includes laboratory assignments, which consist of hands-on experience. Theory of optimization for constrained and unconstrained problems. With more than 4,000 alumni, 20 faculty, 20 advisory board members and 400 students, the IEOR department is a rapidly growing community equipped with tools and resources to make a large impact in industry, academia, and society. The course aims to train students in hands-on statistical, optimization, and data analytics for quantitative portfolio and risk management. Students work on a field project under the supervision of a faculty member. 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. 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. 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. Courses. Analytical techniques for the improvement of manufacturing performance along the dimensions of productivity, quality, customer service, and throughput. Alternate formulations for integer optimization: strength of Linear Programming relaxations. Risk Modeling, Simulation, and Data Analysis. Repeat rules: Course may be repeated for credit with instructor consent. This course will focus on the understanding and use of such tools, to model and solve complex real-world business problems, to analyze the impact of changing data and relaxing assumptions on these decisions, and to understand the risks associated with particular decisions and outcomes. Students work in teams under faculty supervision. Watch, listen, and learn. To train the students in the selection of appropriate techniques to be used for integer optimization problems. This course will cover topics related to healthcare analytics, including: optimizing chronic disease management, designing matching markets for health systems, developing predictive analytics models, and managing resource utilization. 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. Specialized strategies by integer programming solvers. IEOR 130; IEOR 142; IEOR 150; IEOR 151; IEOR 153; IEOR 160; IEOR 161; IEOR 162 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. 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. Linear Programming and Network Flows: Read More [+], Linear Programming and Network Flows: Read Less [-], Terms offered: Spring 2023 The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. Overflow models. Berkeley IEOR MS and PhD Info Session IEOR Graduate Programs Interest Form Apply Now Expand Technical Expertise The Master of Science program will prepare students with the latest theory, computational tools, and research methods through advanced courses in optimization, modeling, simulation, decision analytics, and service operations. Student teams implement an enterprise-scale simulation in a semester-length design project. Relationship to theory of production, inventory theory and hierarchical organization of production management. Formerly Engineering 120. ic packages to solve complex analytics problems; This introductory course provides students with sufficient background in Python programming language Grading Based on: 30% Class Attendance and Participation. Operations Research & Management Science, B.S. 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. Discrete and continuous time Markov chains; with applications to various stochastic systems--such as queueing systems, inventory models and reliability systems. Students work in teams with local companies on a database design project. The course is focused around intensive study of actual business situations through rigorous case-study analysis. Industrial and Commercial Data Systems: Read More [+], Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week, Industrial and Commercial Data Systems: Read Less [-], Terms offered: Spring 2023 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). 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. 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 Course Objectives: Familiarity with algorithm design and mathematical maturity recommended, Fundamentals of Revenue Management: Read Less [-], Terms offered: Fall 2020, Fall 2019, Fall 2018 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. UC Berkeley equivalent courses: Linear algebra: MATH 54, STAT 89A; . Related concepts of computer science tools and theoretical concepts are covered to support the project. Experimenting with Simulated Systems: Read More [+], Prerequisites: 165 or equivalent statistics course, and some computer programming background, Instructors: Ross, Schruben, Shanthikumar, Experimenting with Simulated Systems: Read Less [-], Terms offered: Fall 2022, Fall 2021, Fall 2020 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 [+] Selected topics in mathematical programming. This program prepares you to understand, design, and analyze complex systems through IEOR technical coursework and helps you cultivate an entrepreneurial mindset and develop leadership skills with a degree from Haas. Exposure students to state-of-art advanced simulation techniques. Prerequisites: Students should have taken a probability course, such as STAT134 or INDENG172, and should have programming experience in Matlab or Python. 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. Design and analysis of models and algorithms for facility location, vehicle routing, and facility layout problems. Applications on semiconductor manufacturing or other industrial settings. This course will study and draw connections between disparate fields to trace the development and influence of this view. Advanced seminars in industrial engineering and operations research. Methods for evaluating real options will be presented. 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 Includes formulation of risk problems and probabilistic risk assessments. 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. Credit Restrictions: Students will receive no credit for Ind Eng 171 after taking UGBA105. Grading/Final exam status: Offered for pass/not pass grade only. Basic graduate course in linear programming and introduction to network flows and non-linear programming. Course Objectives: 2. Network Flows and Graphs: Read More [+], Prerequisites: 262A (may be taken concurrently), Terms offered: Spring 2022, Spring 2016, Spring 2015 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. Introduction to Data Modeling, Statistics, and System Simulation: Read More [+]. Credit Restrictions: Ind Eng 242 shares a fair amount of overlapping content with Ind Eng 142. Uncertainty; preference under risk; decision analysis. Development of analytical tools for improving efficiency, customer service, and profitability of production environments. This is a Masters of Engineering course, in which students will develop a fundamental understanding of how randomness and uncertainty are root causes of risk in modern enterprises. 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