Industrial Engineering and Operations Research

Terms offered: Not yet offered
An introduction to computer programming focused on developing foundational skills that can be used for implementing analytics methodologies and software. Programming concepts that will be introduced include: control statements, functions, sequences, dictionaries and sets, vectorization, strings, files and exceptions, object-oriented programming, recursion, search, sort, and databases. Applications of these concepts towards simulation analytics and optimization analytics will also be introduced. There are several homeworks and small-scale programming projects.
Computer Programming for Analytics: Read More [+]

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 [+]

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 [+]

Terms offered: Spring 2019, Fall 2015, Spring 2015
Supervised group study and research by lower division students.
Supervised Group Study and Research: Read More [+]

Terms offered: Prior to 2007
Supervised independent study for lower division students.
Supervised Independent Study and Research: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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: Spring 2023, Spring 2022, Fall 2021
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 [+]

Terms offered: Fall 2023, Spring 2023, Fall 2022
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 [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
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, finance, the Internet, and more. Through these examples, conceptual exercises, data analysis exercises in Python, 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, time series modeling, clustering, principal component analysis, regularization, and large-scale learning with neural networks.
Introduction to Machine Learning and Data Analytics: Read More [+]

Terms offered: Spring 2025, Spring 2024
Following IEOR 142A/242A, this course further introduces students to essential
methodologies and recent trends in machine learning and data analytics. The
course will bridge theoretical foundations with applied data analytics by using
examples and real datasets from domains such as e-commerce, social media, finance,
and more. Students will gain experience with various data analytics packages in
Python and will deliver a comprehensive team project. Topics include: deep
learning, time series and survival analysis, end-to-end learning, causal inference,
reinforcement learning, and ethics, fairness and safety in artificial intelligence.
Machine Learning and Data Analytics II: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
Revenue management (RM) is the decision science of efficiently selling a fixed supply of various goods and services when the demand is heterogeneous and uncertain. This undergraduate course will focus on fundamental models and algorithms for RM. Broad usefulness of concepts will be demonstrated through applications in airline reservation systems, retail, advertising, e-commerce and school-student assignments.
Fundamentals of Revenue Management: Read More [+]

Terms offered: Fall 2024, Fall 2020, Fall 2019
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: Spring 2025, Fall 2022, Fall 2021
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: Fall 2024, Spring 2024, Spring 2022
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: Spring 2025, Spring 2024
With the growing complexity of providing healthcare, it is increasingly important to design and manage health systems using engineering and analytics perspectives. 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.
Healthcare Analytics: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
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: Spring 2025, Fall 2024, Spring 2024
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: Spring 2025, Spring 2024
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 [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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.

Engineering Statistics, Quality Control, and Forecasting: Read More [+]

Terms offered: Spring 2025, Fall 2023, Spring 2022
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 2022, Spring 2021, Fall 2020
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 [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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 [+]

Terms offered: Fall 2024, Fall 2023, Spring 2023
This course emphasizes the three Berkeley Changemaker pillars of critical thinking, effective communication, and productive collaboration. It combines critical examination of entrepreneurial challenges with strategic, ethical, and leadership theories. It develops verbal and collaborative leadership skills, through flipped classroom and intense case discussions, a team project, and a formal final presentation of the project. The case discussions in particular will develop effective listening, real-time analysis, and verbal leadership skills. The project will challenge you to analyze a current or historical ethical challenge in a high technology industry,
or analyze the ethical implications of your own entrepreneurial plans.
Berkeley Changemaker: Ethical and Effective Entrepreneurship in High Tech: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
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. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements.

Probability and Risk Analysis for Engineers: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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 [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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 [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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 [+]

Terms offered: Fall 2017, Spring 2014, Fall 2013
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: Entrepreneurial Marketing and Finance: Read More [+]

Terms offered: Spring 2020, Fall 2019, Spring 2019
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: Read More [+]

Terms offered: Spring 2017, Fall 2014, Spring 2014
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: Read More [+]

Terms offered: Spring 2013, Spring 2012, Spring 2011
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: Read More [+]

Terms offered: Spring 2020, Fall 2019, Spring 2019
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: Read More [+]

Terms offered: Fall 2021, 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 [+]

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 [+]

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 [+]

Terms offered: Fall 2022
Individual study and research for at least one academic year on a special problem approved by a member of the faculty; preparation of the thesis on broader aspects of this work.
Operations Research and Management Science Honors Thesis: Read More [+]

Terms offered: Prior to 2007
Individual study and research for at least one academic year on a special problem approved by a member of the faculty; preparation of the thesis on broader aspects of this work.
Operations Research and Management Science Honors Thesis: Read More [+]

Terms offered: Fall 2024, Spring 2023, Fall 2022
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 [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
Group studies of selected topics. Semester course unit value and contact hours will have a one-to-one ratio.
Directed Group Studies for Advanced Undergraduates: Read More [+]

Terms offered: Fall 2022, Fall 2021, Fall 2020
Supervised independent study. Enrollment restrictions apply.
Supervised Independent Study: Read More [+]

Terms offered: Prior to 2007
This introductory course provides students with sufficient background in Python programming language
for use in analytics applications as well as potentially conducting research in the area. The course is
designed to prepare students for the applied analytics problems and projects they will encounter in
advanced analytics courses. It will start with basic programming topics using Python and cover
using powerful Python packages such as Numpy, Scipy, Pandas, and Matplotlib that are essential for
descriptive, predictive, and prescriptive analytics. Students will work on group projects along with
the instructor in order to solidify the lectures into practical experience using Python for analytics.
Python for Analytics: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
Advanced topics in information management, focusing on design of relational databases, querying, and normalization. New issues raised by the World Wide Web. Research projects on current topics in information technology.
Analysis and Design of Databases: Read More [+]

Terms offered: Spring 2017, Spring 2016, Spring 2015
Analysis of the capacity and efficiency of production systems. Development of analytical tools for improving efficiency, customer service, and profitability of production environments. Design and development of effective industrial production planning systems. Modelling principles are illustrated by reviewing actual large-scale planning systems successfully implemented for naval ship overhaul and for semiconductor manufacturing.
Economics and Dynamics of Production: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
A course on financial concepts useful for engineers that will cover, among other topics, those of interest rates, present values, arbitrage, geometric Brownian motion, options pricing, & portfolio optimization. The Black-Scholes option-pricing formula will be derived and studied. 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. Portfolio optimization problems will be considered both from a mean-variance and from a utility function point of view. Methods for evaluating real options will be presented. The use of mathematical optimization models as a framework for analyzing financial engineering problems will be shown.
Introduction to Financial Engineering: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
Introductory graduate level course, focusing on applications of operations research techniques, e.g., probability, statistics, and optimization, to financial engineering. The course starts with a quick review of 221, including no-arbitrage theory, complete market, risk-neutral pricing, and hedging in discrete model, as well as basic probability and statistical tools. It then covers Brownian motion, martingales, and Ito's calculus, and deals with risk-neutral pricing in continuous time models. Standard topics include Girsanov transformation, martingale representation theorem, Feyman-Kac formula, and American and exotic option pricings. Simulation techniques will be discussed at the end of the semester, and MATLAB (or C or S-Plus) will be used for computation.
Financial Engineering Systems I: Read More [+]

Terms offered: Spring 2024, Spring 2023, Fall 2022
Advanced graduate course for Ph.D. students interested in pursuing a professional/research career in financial engineering. The course will start with a quick review of 222: the basics of Brownian motion, martingales, Ito's calculus, risk-neutral pricing in continuous time models. It then covers rigorously and in depth the most fundamental probability concepts for financial engineers, including stochastic integral, stochastic differential equations, and semi-martingales. The second half of the course will discuss the most recent topics in financial engineering, such as credit risk and analysis, risk measures and portfolio optimization, and liquidity risk and models.
Financial Engineering Systems II: Read More [+]

Terms offered: Spring 2019, Spring 2018
The course aims to train students in hands-on statistical, optimization, and data analytics for quantitative portfolio and risk management. In addition, the course will introduce elements of financial markets and asset classes. 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. Students will undertake computational assignments and a group project. They will also manage hypothetical portfolios throughout the course.
Portfolio and Risk Analytics: Read More [+]

Terms offered: Prior to 2007
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. The course includes laboratory assignments, which consist of hands-on experience.
Introduction to Convex Optimization: Read More [+]

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017
Convex optimization as a systematic approximation tool for hard decision problems. 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. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics, control, finance, data mining, operations research.
Convex Optimization and Approximation: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
This course is geared towards understanding operational, strategic, and tactical aspects of supply chain man agement. Topics covered are from a broad range that includes demand modeling, inventory management, facility location as well as process flexibility, contracting, and auctions. 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. Students will be exposed to the key concepts through a mixture of foundational theory and case studies from a variety of businesses. The course is intended for graduate students at the Masters level looking for a concrete introduction
Economics of Supply Chains: Read More [+]

Terms offered: Spring 2025, Fall 2023, Spring 2023
This course uses simulation models for analyzing and optimizing systems where the underlying processes and/or parameters are not fully known, but data may be available, sampled, or artificially generated. Monte Carlo simulations are used in a weekly laboratory to model systems that may be too complex to approximate accurately with deterministic, stationary, or static models; and to measure the robustness of predictions and manage risks in decisions based on data-driven models.
Introduction to Data Modeling, Statistics, and System Simulation: Read More [+]

Terms offered: Spring 2023, Spring 2022, Fall 2021
This is an advanced project course in data science that offers a "maker" and/or "innovation" viewpoint. The course is focused first on developing an open-ended-real world project relating to data science. Related concepts of computer science tools and theoretical concepts are covered to support the project. These concepts include filtering, prediction, classification, LTI systems, and spectral analysis. 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.
Applied Data Science with Venture Applications: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
Computing technology has advanced to the point that commonly available tools can be used to solve practical decision problems and optimize real-world systems quickly and efficiently. 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.
Optimization Analytics: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
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. The technical material will be presented in the context of engineering team system design and operations decisions.
Risk Modeling, Simulation, and Data Analysis: Read More [+]

Terms offered: Fall 2024, Spring 2024
This course applies foundational concepts in programming, databases, machine learning, and statistical modeling to answer questions from business and social science. The goal is for students to develop the experience and intuition to gather and build new datasets and answer substantive questions.

Machine Learning and Data Analytics: Read More [+]

Terms offered: Spring 2025, Spring 2024
Following IEOR 142A/242A, this course further introduces students to essential
methodologies and recent trends in machine learning and data analytics. The
course will bridge theoretical foundations with applied data analytics by using
examples and real datasets from domains such as e-commerce, social media, finance,
and more. Students will gain experience with various data analytics packages in
Python and will deliver a comprehensive team project. Topics include: deep
learning, time series and survival analysis, end-to-end learning, causal inference,
reinforcement learning, and ethics, fairness and safety in artificial intelligence.
Machine Learning and Data Analytics II: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
A project course to provide hands-on experience in end-to-end analytics development from
exploratory analytics to systems analytics in an industry context, including communication of
recommendations. Students will work in teams on projects and build solutions to
business/industry challenges using Python packages such as Pandas, NumPy, Matplotlib, scikit-
learn, Bokeh, and relevant optimization and simulation software.
Analytics Lab: Read More [+]

Terms offered: Fall 2024, Fall 2023
This course will introduce students to the science of engineering demand given a fixed supply of various goods and services when the customer base is heterogeneous and uncertain (a.k.a. Revenue Management). This requires an understanding of customer choice behaviour, different pricing and allocation strategies, and mechanisms for customer and seller interaction. The course will focus on introducing students to (i) Models that capture the key dynamics of these problems and (ii) Algorithmic ideas that turn these models into actionable decisions. The broad usefulness of concepts covered will be demonstrated through several applications such as in airline reservation systems, retail, advertising, e-commerce as well as non-profit applications.
Fundamentals of Revenue Management: Read More [+]

Terms offered: Fall 2013
This course introduces you to the field of supply chain management through a series of lectures and case studies that emphasize innovative concepts in supply chain management that have proven to be beneficial for a good number of adopters. Innovations that we will discuss include collaborative forecasting, social media, online procurement, and technologies such as RFID.
Supply Chain Innovation, Strategy, and Analytics: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
This will be an introductory first-year graduate course covering fundamental models in production planning and logistics. 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.
Introduction to Production Planning and Logistics Models: Read More [+]

Terms offered: Fall 2012, Spring 2005, Spring 2004
Design and analysis of models and algorithms for facility location, vehicle routing, and facility layout problems. Emphasis will be placed on both the use of computers and the theoretical analysis of models and algorithms.
Facilities Design and Logistics: Read More [+]

Terms offered: Spring 2021, Spring 2014, Spring 2013
This course focuses on the design of service businesses such as commercial banks, hospitals, airline companies, call centers, restaurants, Internet auction websites, and information providers. The material covered in the course includes internet auctions, procurement, service facility location, sevice quality management, capacity planning, airline ticket pricing, financial plan design, pricing of digital goods, call center management, service competition, revenue management in queueing systems, information intermediaries, and health care. The goal of the instructors is to equip the students with sufficient technical background to be able to do research in this area.
Service Operations Management: Read More [+]

Terms offered: Spring 2013, Spring 2012, Spring 2011
Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. The field has made significant strides on both theoretical and practical fronts. On the theoretical front, supply chain analysis inspires new research ventures that blend operations research, game theory, and microeconomics. These ventures result in an unprecedented amalgamation of prescriptive, descriptive, and predictive models characteristic of each subfield. On the practical front, supply chain analysis offers solid foundations for strategic positioning, policy setting, and decision making.
Supply Chain Operation and Management: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023, Spring 2022
Supply chain analysis is the study of quantitative models that characterize various economic trade-offs in the supply chain. The field has made significant strides on both theoretical and practical fronts. On the theoretical front, supply chain analysis inspires new research ventures that blend operations research, game theory, and microeconomics. These ventures result in an unprecedented amalgamation of prescriptive, descriptive, and predictive models characteristic of each subfield. On the practical front, supply chain analysis offers solid foundations for strategic positioning, policy setting, and decision making.
Supply Chain and Logistics Management: Read More [+]

Terms offered: Spring 2014, Fall 2011, Fall 2009
Mathematical and computer methods for design, planning, scheduling, and control in manufacturing and distribution systems.
Production and Inventory Systems: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
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. The main focus is on design and analysis of models and algorithms for matching, pricing, incentivizing, and personalizing on such platforms. Sample topics include, but are not limited to, resource allocation and pricing under uncertain sequential demand, mechanism design, discrete choice models, static and dynamic assortment optimization, real-time recommendations, spatial supply response and supply re-balancing in bike/ride sharing systems.
Frontiers in Revenue Management: Read More [+]

Terms offered: Spring 2025, Spring 2024
With the growing complexity of providing healthcare, it is increasingly important to design and manage health systems using engineering and analytics perspectives. 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.
Heathcare Analytics: Read More [+]

Terms offered: Spring 2019, Spring 2017
One of the grand challenges of this century is the modernization of electrical power networks. This graduate-level course provides a fundamental understanding of the mathematics behind the operation of power grids.
Control and Optimization for Power Systems: Read More [+]

Terms offered: Spring 2009, Spring 2007, Spring 2006
This course will introduce graduate and upper division undergraduate students to modern methods for simulating discrete event models of complex stochastic systems. About a third of the course will be devoted to system modeling, with the remaining two-thirds concentrating on simulation experimental design and analysis.
Experimenting with Simulated Systems: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
Basic graduate course in linear programming and introduction to network flows and non-linear programming. Formulation and model building. The simplex method and its variants. Duality theory. Sensitivity analysis, parametric programming, convergence (theoretical and practical). Polynomial time algorithms. Introduction to network flows models. Optimality conditions for non linear optimization problems.
Mathematical Programming I: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
Basic first year graduate course in optimization of non-linear programs. Formulation and model building. Theory of optimization for constrained and unconstrained problems. Study of algorithms for non-linear optimization with emphasis on design considerations and performance evaluation.
Mathematical Programming II: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
Conditional Expectation. Poisson and general point process and renewal theory. Renewal reward processes with application to inventory, congestion, and replacement models. Discrete and continuous time Markov chains; with applications to various stochastic systems--such as queueing systems, inventory models and reliability systems.
Applied Stochastic Process I: Read More [+]

Terms offered: Spring 2025, Spring 2024, Spring 2023
Continuous time Markov chains. The reversed chain concept in continuous time Markov chains with applications of queueing theory. Semi-Markov processes with emphasis on application. Brownian Motion. Random walks with applications. Introduction to Martinjales.
Applied Stochastic Process II: Read More [+]

Terms offered: Spring 2017, Spring 2016, Spring 2015
This course is on computational methods for the solution of large-scale optimization problems. The focus is on converting the theory of optimization into effective computational techniques. Course topics include an introduction to polyhedral theory, cutting plane methods, relaxation, decomposition and heuristic approaches for large-scale optimization problems.
Computational Optimization: Read More [+]

Terms offered: Spring 2022, Fall 2021, Spring 2021
This course will cover topics related to the interplay between optimization and statistical learning. The first part of the course will cover statistical modeling procedures that can be defined as the minimizer of a suitable optimization problem. 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. 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.
Learning and Optimization: Read More [+]

Terms offered: Fall 2024, Spring 2024, Fall 2022
Survey of solution techniques and problems that have formulations in terms of flows in networks. Max-flow min-cut theorem. Minimum cost flows. Multiterminal and multicommodity flows. Relationship with linear programming, transportation problems, electrical networks and critical path scheduling.
Network Flows and Graphs: Read More [+]

Terms offered: Spring 2025, Spring 2022, Spring 2016
The result "L = (lambda) w" and other conservation laws. Elementary queueing models; comparing single- and multiple-server queues. PASTA. Work. Markovian queues; product form results. Overflow models. Embedded Markov chains. Random walks and the GI/G/l queues. Work conservation; priorities. Bounds and approximations.
Queueing Theory: Read More [+]

Terms offered: Fall 2024, Fall 2021, Spring 2018
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. Brief introduction to decision making under risk and uncertainty.
Applied Dynamic Programming: Read More [+]

Terms offered: Spring 2020, Spring 2010, Spring 2009
The course deals with discrete optimization problems and their complexity. 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.
Integer Programming and Combinatorial Optimization: Read More [+]

Terms offered: Fall 2015, Fall 2014
This seminar and discussion class aims to survey current and classic research on innovation and help
doctoral students formulate their research designs. Readings are drawn from economics, organizations,
and other social sciences, and engineering and in particular, data science research on analyzing large
data sets. Students develop research designs and present each week and formally for their final. A
written paper is also required. Authors join us, physically or virtually.
Current Readings in Innovation: Read More [+]

Terms offered: Spring 2011, Spring 2010, Spring 2009
A project course for students interested in applications of operations research and engineering methods. One or more systems, which may be public or in the private sector, will be selected for detailed analysis and re-designed by student groups.
Systems Analysis and Design Project: Read More [+]

Terms offered: Prior to 2007
Automation is a central aspect of contemporary industrial engineering that combines sensors, actuators, and computing to monitor and perform operations. It is applied to a broad range of applications from manufacturing to transporation to healthcare. This course provides an introduction to analysis, models, algorithms, research, and practical skills in the field and includes a laboratory component where students will learn and apply basic skills in computer programming and interfacing of sensors and motors that will culminate in a team design project.
Automation Science and Engineering: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
Lectures and appropriate assignments on fundamental or applied topics of current interest in industrial engineering and operations research.
Special Topics in Industrial Engineering and Operation Research: Read More [+]

Terms offered: Spring 2014, Fall 2008, Spring 2008
Development of dynamic activity analysis models for production planning and scheduling. Relationship to theory of production, inventory theory and hierarchical organization of production management.
Dynamic Production Theory and Planning Models: Read More [+]

Terms offered: Spring 2017, Spring 2014, Spring 2011
Selected topics in mathematical programming. 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).
Advanced Mathematical Programming: Read More [+]

Terms offered: Spring 2016, Spring 2015, Spring 2014
Seminar on selected topics from financial and technological risk theory, such as risk modeling, attitudes towards risk and utility theory, portfolio management, gambling and speculation, insurance and other risk-sharing arrangements, stochastic models of risk generation and run off, risk reserves, Bayesian forecasting and credibility approximations, influence diagrams, decision trees. Topics will vary from year to year.
Topics in Risk Theory: Read More [+]

Terms offered: Spring 2025, Fall 2024, Spring 2024
Advanced seminars in industrial engineering and operations research.
Group Studies, Seminars, or Group Research: Read More [+]

Terms offered: Fall 2023, Summer 2023 Second 6 Week Session, Fall 2019
Individual investigation of advanced industrial engineering problems.
Individual Study or Research: Read More [+]

Terms offered: Fall 2024, Fall 2023, Fall 2022
This course provides basic training for graduate student instructors (GSIs). Discussion, practice, and review of fundamentals, issues, and best practices in teaching for any engineering course. 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.
GSI Proseminar on Teaching Engineering: Read More [+]

Terms offered: Fall 2010, Fall 2008, Spring 2008
Individual study for the comprehensive in consultation with the field adviser. Units may not be used to meet either unit or residence requirements for a master's degree.
Individual Study for Master's Students: Read More [+]

Terms offered: Fall 2010, Spring 2008, Fall 2007
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). May not be used for unit or residence requirements for the doctoral degree.
Individual Study for Doctoral Students: Read More [+]