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Spring 2022 Industrial Engineering and Operations Research E3404 section 001

Call Number 12712
Day & Time
MW 4:10pm-5:25pm
329 Pupin Laboratories
Points 4
Grading Mode Standard
Approvals Required None
Instructor Yi Zhang
Method of Instruction In-Person
Course Description It is strongly advised that Stochastic modeling (IEOR E3106 or IEOR E4106) be taken before this course. This is an introductory course to simulation, a statistical sampling technique that uses the power of computers to study complex stochastic systems when analytical or numerical techniques do not suffice. The course focuses on discrete-event simulation, a general technique used to analyze a model over time and determine the relevant quantities of interest. Topics covered in the course include the generation of random numbers, sampling from given distributions, simulation of discrete-event systems, output analysis, variance reduction techniques, goodness of fit tests, and the selection of input distributions. The first half of the course is oriented toward the design and implementation of algorithms, while the second half is more theoretical in nature and relies heavily on material covered in prior probability courses. The teaching methodology consists of lectures, recitations, weekly homework, and both in-class and take-home exams. Homework almost always includes a programming component for which students are encouraged to work in teams.
Web Site Vergil
Department Industrial Engineering and Operations Research
Enrollment 63 students (70 max) as of 8:03AM Saturday, June 25, 2022
Subject Industrial Engineering and Operations Research
Number E3404
Section 001
Division School of Engineering and Applied Science: Undergrad
Campus Morningside
Section key 20221IEOR3404E001

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SIS update 06/25/22 08:03    web update 06/25/22 17:34