Operational planning and long term planning for companies are more complex in recent years. Information change fast, and the decision making is a hard task. Therefore, optimization algorithms (operational research) are used to find optimal solutions for these problems. Professionals in this field are the most valued ones.
In this course you will learn what is necessary to solve problems applying Mathematical Optimization:
Linear Programming (LP)
Mixed-Integer Linear Programming (MILP)
NonLinear Programming (NLP)
Mixed-Integer Linear Programming (MINLP)
Genetic Algorithm (GA)
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
Same Packages and tools: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter Notebook
Moreover, you will learning 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
Maximize the revenue in a rental car store
Optimal Power Flow: Electrical Systems
The classes use examples that are created step by step, so we will create the algorithms together.
Besides this course is more concerned with mathematical approaches, you will also learn how to solve problems using artificial intelligence (AI), genetic algorithm, and particle swarm.
Don't worry if you do not know Python or how to code, I will teach you everything you need to start with optimization, from the installation of Python and its basics, to complex optimization problems.
I hope this course can help you in your carrier. Yet, you will receive a certification from Udemy.
Operations Research | Operational Research | Mathematical Optimization
See you in the classes!