Optimization with GAMS: Operations Research Bootcamp A-Z
What you'll learn
- Basic Concepts and Terms Related to Optimization
- How to Formulate a Mathematical Problem
- Linear Programming and Coding LP Problems in GAMS
- Mixed Integer Linear Programming (MILP) and Coding MILP Problems in GAMS
- Non-Linear Programming (NLP) and Coding NLP Problems in GAMS
- Mixed Integer Non-Linear Programming (MINLP) and Coding MINLP Problems in GAMS
- Multi Objective Optimization
- Sequential Goal Programming and How to Code a SGP Problem in GAMS
- There is no prerequisites since this course is designed for complete beginners to mathematical optimization and I start from downloading and installing GAMS and prepare students for the course.
The art of decision making and finding the optimal solution to a problem is getting more and more attention in recent years. In this course, you will learn how to deal with various types of mathematical optimization problems as below:
Linear Programming (LP)
Mixed Integer Linear Programming (MILP)
Mixed Integer Non-Linear Programming
We start from the beginning that you need to formulate a problem. Therefore, after finishing this course, you will be able to find and formulate decision variables, objective function, constraints and define your parameters. Moreover, you will learn how to develop the model that you formulated in the GAMS environment. Using GAMS, you will learn how to:
Define Sets, Parameters, Scalars, Objective Function & Constraints
Import and read data from an external source (Excel file)
Solve the optimization problem using various solvers such as CPLEX, IPOPT, COUENNE, BONMIN, ...
Create a report from your result in GAMS results
Export your results into an external source (Excel file)
Deal with multi-objective problems and solve them using GAMS solvers
In this course, we solve simple to complex optimization examples from engineering, production management, scheduling, transportation, supply chain, and ... areas.
This course is structured based on 3 examples for each of the main mathematical programming sections. In the first two examples, you will learn how to deal with that type of specific problem. Then you will be asked to challenge yourself by developing the challenge problem into GAMS. However, even the challenge problem will be explained and solved with details.
Who this course is for:
- Students in all levels (Undergrad, Grad and PhD)
- Professionals in Various disciplines such as Engineering, Management and Operation Research
- Companies Who Wants to Use Optimization in Their Businesses
- Anyone Who is Interested to Learn Optimization!
My name is Navid Shirzaid and I am super excited that you are here to read this section!
I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies.
I am also proficient in coding with Python and developing machine learning and deep learning models for different applications.
I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence.
To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you!