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The [course_title] course teaches you the optimization and analysis procedures of a manufacturing system for decision support.

Throughout the course, you will learn about modelling, optimization, and simulation of the manufacturing system. In the Optimization models and algorithms section, you will be introduced with a framework to think about a wide range of issues that arise in manufacturing systems. The next part exposes you to a broad range of applications for these methods and models, and to integrate this material with their introduction to operations management.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT 

Course Curriculum

Module 01
Introduction and Overview 00:30:00
LP (Linear Programming) – Introduction, Formulations and Geometry 00:25:00
LP (Linear Programming) – Example 00:30:00
Networks – Introduction 00:30:00
LP (Linear Programming) 00:30:00
LP (Linear Programming) – Solution Methods and Sensitivity Analysis 00:30:00
Integer Programming – Formulations 00:30:00
Integer Programming – Algorithms 00:30:00
Introduction, Simulation Process and Stochastic Modeling 00:35:00
Module 02
Monte-Carlo Theory and Examples 00:30:00
Ontario Gateway Case Discussion 00:30:00
Experimental Design and Simulation Analysis 00:30:00
Advanced Modeling, Simulation-Based Optimization 00:30:00
Non Linear Programming – Formulations 00:30:00
NLP (Non Linear Programming) – Examples and Algorithms 00:30:00
Langrangean Example 00:30:00
Project Presentations and Class Summary 00:30:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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