Udemy

Optimization with Python: Solve Operations Research Problems

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  • 13,499 Students
  • Updated 3/2025
4.5
(1,888 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
12 Hour(s) 47 Minute(s)
Language
English
Taught by
Rafael Silva Pinto
Rating
4.5
(1,888 Ratings)

Course Overview

Optimization with Python: Solve Operations Research Problems

Solve optimization problems with CPLEX, Gurobi, Pyomo... using linear programming, nonlinear, evolutionary algorithms...

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 Programming (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

  • Maximize the revenue in a rental car store

  • Optimal Power Flow: Electrical Systems

  • Many other examples, some simple, some complexes, including summations and many constraints.


The classes use examples that are created step by step, so we will create the algorithms together.

Besides this course is more focused in 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. Also, I have created a nice introduction on mathematical modeling, so you can start solving your problems.

I hope this course can help you in your career. Yet, you will receive a certification from Udemy.


Operations Research | Operational Research | Mathematical Optimization


See you in the classes!!

Course Content

  • 10 section(s)
  • 104 lecture(s)
  • Section 1 Introduction to the course
  • Section 2 Installing Python
  • Section 3 Starting with Python
  • Section 4 Introduction to mathematical modelling
  • Section 5 Linear Programming (LP)
  • Section 6 Working with Pyomo
  • Section 7 Mixed-Integer Linear Programming (MILP)
  • Section 8 Nonlinear Programming (NLP)
  • Section 9 Mixed-Integer Nonlinear Programming (MINLP)
  • Section 10 Genetic Algorithm and Particle Swarm

What You’ll Learn

  • Solve optimization problems using linear programming, mixed-integer linear programming, nonlinear programming, mixed-integer nonlinear programming,
  • LP, MILP, NLP, MINLP, SCOP, NonCovex Problems
  • Main solvers and frameworks, including CPLEX, Gurobi, and Pyomo
  • Genetic algorithm, particle swarm, and constraint programming
  • From the basic to advanced tools, learn how to install Python and how to use the main packages (Numpy, Pandas, Matplotlib...)
  • How to solve problems with arrays and summations


Reviews

  • R
    Rachana Gupta
    3.0

    It was an okayish course. Instructor's accent and the level of engagement you get listening to him is very very less. There are also lots of videos with 10-30 minute pauses so you could do the work alongside (this I felt was unnecessary) - it was impossible to skip those pause sections when using a company's udemy business login.

  • A
    Abhishek
    4.5

    Brilliant course. Absolute gold. Good deep dive into various frameworks and solvers on python and very clearly explained the implementation. Highly recommend this course

  • F
    F. Rafael Reyes
    4.5

    Clear explanations and good examples on a subject with limited resources online.

  • C
    Carlos Candelo Zuluaga
    5.0

    Amazing and direct to the point.

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