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Operational planning and long term planning for companies are more complex in recent years. Information changes fast, and the decision making is a hard task. Therefore, optimization algorithms (operations research) are used to find optimal solutions for these problems. Professionals in this field are one of the most valued in the market.
In this course you will learn what is necessary to solve problems applying Mathematical Optimization and Metaheuristics:
Linear Programming (LP)
Mixed–Integer Linear Programming (MILP)
NonLinear Programming (NLP)
Mixed–Integer Linear Programming (MINLP)
Genetic Algorithm (GA)
Multi–Objective Optimization Problems with NSGA–II (an introduction)
Particle Swarm (PSO)
Constraint Programming (CP)
Second–Order Cone Programming (SCOP)
NonConvex Quadratic Programmin (QP)
The following solvers and frameworks will be explored:
Solvers: CPLEX – Gurobi – GLPK – CBC – IPOPT – Couenne – SCIP
Frameworks: Pyomo – Or–Tools – PuLP – Pymoo
Same Packages and tools: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter Notebook
Moreover, you will learn how to apply some linearization techniques when using binary variables.
In addition to the classes and exercises, the following problems will be solved step by step:
Optimization on how to install a fence in a garden
Route optimization problem
Specification: Optimization with Python: Solve Operations Research Problems
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12 reviews for Optimization with Python: Solve Operations Research Problems
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| Price | $12.99 |
|---|---|
| Provider | |
| Duration | 12.5 hours |
| Year | 2022 |
| Level | All |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
$12.99
Nick B –
The course offers a nice overview on optimization and practical examples on how to solve many type of problems
Junkyu Park –
The course provides the contents that I am exactly looking for! The optimization applied to python. This course provides good overview of optimization problem solving with python.
Furkan Kasa –
One of the best I’ve ever get in Udemy. There is no unnecessary or unrelated information. Simple and short path that leads to you for your goal with good explanations of the teacher.
Adi Krish –
The instructor put a lot of work into this course. I really appreciate his effort, he does not go into unneeded/unwanted detail. I would recommend this course to many people :).
Niccol Bitossi –
Good structure. Very hands on. Theory left at a minimum level. It’s a good course if you already know a bit about mathematical optimisation and are interested in learning how to solve optimisation problems with Python.
Ali Guzel –
just PERFECT!!!
Conor Phelan –
Comprehensive course covering everything you will need to solve optimization problems using python. Beneficial for newcomers as well as those experienced with python optimization. Covers all the basics very well and transitions into the more challenging real world problems towards the end. 10/10 would recommend. Some lessons are unavailable in english but I believe the translations are currently being prepared. Excellent course.
RalphU –
Excelent
Joy Biswas –
Explained nicely till now.
Chanon Krittapholchai –
Good Teaching & Good Material. This course make me don’t want to use PuLP again.
ZOHEIB TUFAIL KHAN –
Best teaching method and material. One of the beast course for beginners as well as for advance users. This course covers all the concepts. The explanation is very clear.
Alex Dou –
Awesome and logical structure, spot on and sharp content