Optimization with Python- Solve Operations Research Problems

Optimization with Python: Solve Operations Research Problems, available at $84.99, has an average rating of 4.45, with 104 lectures, 2 quizzes, based on 1522 reviews, and has 10725 subscribers.

You will learn about 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 This course is ideal for individuals who are Undergrad, graduation, master program, and doctorate students. or Companies that wish to solve complex problems or People interested in complex problems and artificial inteligence It is particularly useful for Undergrad, graduation, master program, and doctorate students. or Companies that wish to solve complex problems or People interested in complex problems and artificial inteligence.

Enroll now: Optimization with Python: Solve Operations Research Problems

Summary

Title: Optimization with Python: Solve Operations Research Problems

Price: $84.99

Average Rating: 4.45

Number of Lectures: 104

Number of Quizzes: 2

Number of Published Lectures: 104

Number of Published Quizzes: 2

Number of Curriculum Items: 106

Number of Published Curriculum Objects: 106

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Undergrad, graduation, master program, and doctorate students.

Companies that wish to solve complex problems

People interested in complex problems and artificial inteligence

Target Audiences

Undergrad, graduation, master program, and doctorate students.

Companies that wish to solve complex problems

People interested in complex problems and artificial inteligence

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 problemswill 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 Curriculum

Chapter 1: Introduction to the course

Lecture 1: Introduction

Lecture 2: What is optimization

Chapter 2: Installing Python

Lecture 1: Installing Python

Lecture 2: Packages

Lecture 3: Important note about Python

Lecture 4: IDE Spyder

Lecture 5: Jupyter NotebookLab

Chapter 3: Starting with Python

Lecture 1: Lists, Tuples, and Dictionary

Lecture 2: If, For, While

Lecture 3: Functions

Lecture 4: Numpy

Lecture 5: Pandas

Lecture 6: Pandas: reading Excel

Lecture 7: Graphs

Lecture 8: PDFs to learn more about Python

Chapter 4: Introduction to mathematical modelling

Lecture 1: What is Mathematical Modelling?

Lecture 2: How do we solve optimization problems?

Lecture 3: Type of Variables

Lecture 4: Objective Function and Constraints

Lecture 5: How to model your problem?

Lecture 6: Our first formulation

Lecture 7: Example 1: investiment

Lecture 8: Example 2: investiment

Lecture 9: Example 3: production cost

Lecture 10: Example 4: route problem

Lecture 11: Example 5: construction assignment

Lecture 12: Example 6: construction assignment

Lecture 13: Example 7: job assignment

Lecture 14: Example 8: job assignment

Lecture 15: How to Learn More?

Lecture 16: Some references for you learn more (problems of VRPTW, TSP, JobShop)

Chapter 5: Linear Programming (LP)

Lecture 1: LP: Introduction

Lecture 2: Framework and Solvers

Lecture 3: LP: Ortools

Lecture 4: LP: SCIP

Lecture 5: LP: SCIP | errors during installation

Lecture 6: LP: Gurobi, CPLEX, and GLPK (installation)

Lecture 7: Academic License for Gurobi [Updates]

Lecture 8: LP: Pyomo (using Gurobi, CPLEX, and GLPK)

Lecture 9: LP: Pyomo | overcoming errors

Lecture 10: LP: PuLP

Lecture 11: Which solver and frameworks should we choose?

Lecture 12: LP: Exercise, solve it by yourself

Lecture 13: LP: Concepts

Chapter 6: Working with Pyomo

Lecture 1: Pyomo: Using other solvers (CBC)

Lecture 2: Pyomo: Summations

Lecture 3: Pyomo: Double Summations and Variables with 2 or more indexes

Lecture 4: Pyomo: Pprint

Lecture 5: Pyomo: Manual

Chapter 7: Mixed-Integer Linear Programming (MILP)

Lecture 1: MILP: Introduction

Lecture 2: MILP: Pyomo

Lecture 3: MILP: Ortools

Lecture 4: MILP: SCIP

Lecture 5: MILP: Exercise, solve it by yourself

Lecture 6: MILP: Exercise solution

Lecture 7: MILP: Concepts

Chapter 8: Nonlinear Programming (NLP)

Lecture 1: NLP: Introduction

Lecture 2: NLP: Pyomo (IPOPT)

Lecture 3: NLP: SCIP

Lecture 4: NLP: Exercise, solve it by yourself

Lecture 5: NLP: Exercise Solution

Lecture 6: NLP: Concepts

Chapter 9: Mixed-Integer Nonlinear Programming (MINLP)

Lecture 1: MINLP: Introduction

Lecture 2: MINLP: Pyomo (Couenne)

Lecture 3: MINLP: Pyomo (decomposition using mindtpy)

Lecture 4: MINLP: SCIP

Chapter 10: Genetic Algorithm and Particle Swarm

Lecture 1: Genetic Algorithm: Introduction

Lecture 2: Genetic Algorithm: Base Case Example

Lecture 3: Genetic Algorithm: Routing Problem

Lecture 4: Multi-Objective Problems using NSGA-II – An introduction

Lecture 5: Particle Swarm (PSO): Base Case Example

Lecture 6: PSO: Concepts

Chapter 11: Constraint Programming (CP)

Lecture 1: CP: Ortools

Lecture 2: CP: Concepts

Chapter 12: Special Cases

Lecture 1: Introduction

Lecture 2: SCOP: Second-Order Cone Programming

Lecture 3: NonConvex Quadratic Programming

Lecture 4: Vehicle Routing Problems (VRP) with Or-Tools, An introduction

Lecture 5: Linearization: binary*continuos using BigM

Lecture 6: Linearization: binary*binary

Chapter 13: Advanced Features for Pyomo

Lecture 1: Introduction and a new Case Study with double summations

Lecture 2: Check the solver progress

Lecture 3: Define a Gap Limit

Lecture 4: Define a Time Limit

Lecture 5: More parameters for the solvers

Instructors

Rafael Silva Pinto
Optimization and Data Science Consultant, PhD

Rating Distribution

1 stars: 7 votes

2 stars: 15 votes

3 stars: 127 votes

4 stars: 509 votes

5 stars: 864 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!