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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
|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|
|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|
|Project Presentations and Class Summary||00:30:00|
|Submit Your Assignment||00:00:00|
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