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Numerical Optimization and Operations Research in Python
Rating: 4.4 out of 5(101 ratings)
1,152 students

Numerical Optimization and Operations Research in Python

Use data efficiently to support decision-making exploring Operations Research and Optimization in Python
Created byBruno Leite
Last updated 1/2024
English

What you'll learn

  • Gain proficiency in solving optimization problems using popular solvers, and learn to interpret and implement their results effectively
  • Learn and apply useful modeling techniques to classical operations research problems
  • Identify and formulate real-world problems as numerical optimization models
  • Complete a case study on how to combine operations research and software engineering to build powerful solutions

Course content

8 sections • 65 lectures • 4h 29m total length

  • Introduction5:23
  • Download Python2:40
  • Download VS Code2:26
  • Configure your project5:08

    Please, after completing this lesson, download the zip file attached to it and extract its contents. Then, follow carefully the tutorial of this lesson to set up your Python virtual environment. Remember, to preview the render contents of a markdown file (such as README.md), you can press Ctrl+Shift+V or Cmd+Shift+V on MacOS.

  • Selecting Python venv from Jupyter in VS Code0:24
  • The Elements of an Optimization Model4:57
  • Additional Notes on Constraints0:34
  • The Knapsack Problem - Definitions3:10
  • Pyomo basics4:54
  • Exercise - The Knapsack Problem0:05
  • The Knapsack Problem - Pyomo8:35
  • Usual Definitions in Numerical Optimization3:11
  • Additional Resource - Nonlinear Programming0:41
  • Course Overview2:20

Requirements

  • Basic programming
  • No previous experience with optimization solvers is required
  • Student might have a better understanding of some sections if familiar with discrete mathematics and linear algebra

Description

Numerical Optimization and Operations Research in Python

Use data efficiently to support decision-making by applying numerical optimization and operations research concepts seen throughout this comprehensive course. It combines theoretical foundations and practical coding applications, designed to empower you with the skills needed to tackle complex problems in a professional or academic context.

You will learn:

  • Theory:

    • Principles of Mathematical Optimization

    • Linear programming (LP)

    • Integer and Mixed-integer linear programming (MILP)

    • Handle infeasible scenarios

    • Multi-objective hierarchical (lexicographic) formulations

    • Constructive Heuristics and Local Search

  • Software:

    • Pyomo

    • Google OR-Tools

    • HiGHS

  • Problems:

    • Knapsack

    • Product-Mix

    • Transportation

    • Lot-Sizing

    • Job-Shop Scheduling

    • Facility Dispersion

    • Traveling Salesman

    • Capacitated Vehicle Routing Problem

  • Industry-Grade Skills: By the end of this course, you'll be able to formulate and solve your own optimization problems, a highly sought-after competency in industries ranging from logistics to finance. You'll also be able to convert your models into scalable programs for your company or team even though they are not familiar with optimization.

Who is this course for?

  • Data scientists and engineers who want to add optimization skills to their toolkit.

  • Professionals in logistics, supply chain management, or finance, who are looking to leverage optimization for decision-making.

  • Academics and students seeking a practical application of operations research and optimization theories.

Course Features:

  • More than 4 hours of comprehensive video lectures explaining concepts in a clear and engaging manner.

  • 13+ Interactive Python notebooks for hands-on practice (and corresponding solutions).

  • Carefully selected articles and external references to improve your learning experience.

  • Access to a community forum for discussion and networking with fellow learners.

  • Lifetime access to course materials, including future updates.

Embark on this journey to master decision-making using optimization in Python. Whether you aim to advance your career, academically explore operations research, or simply enjoy the thrill of solving complex problems, this course is your gateway to new possibilities.

Who this course is for:

  • Professionals in pursuit of essential quantitative decision-making skills
  • Academics eager to learn practical software skills to apply optimization theory

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