Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Operations Research & Optimization Projects With Python
Rating: 4.0 out of 5(78 ratings)
1,024 students

Operations Research & Optimization Projects With Python

Mastering Optimization Techniques: From Linear Programming to Machine Learning-Enhanced Algorithms
Created byAdvancedor Academy
Last updated 3/2026
English

What you'll learn

  • Master the use of Python for optimizing supply chains and factory operations.
  • Build Python models to plan manpower effectively and optimize network flows.
  • Leverage Python libraries for scheduling, routing, and inventory management simulations.
  • Solve complex facility location and capacity problems using Python-based algorithms.
  • Utilize Python for advanced operations research techniques like stochastic processes, game theory, and dynamic programming.
  • Integrate multi-objective decision-making tools for enhanced operational strategies.
  • Implement robust optimization and stochastic models to manage uncertainty in operations.

Course content

91 sections • 313 lectures • 31h 31m total length

  • Introduction2:16

    Explore optimization foundations and operations research, mastering linear programming and stochastic models through practical Python applications to solve real-world problems and improve operational efficiency.

  • Before The Course5:15

    Emphasize active learning by typing out code yourself and watching working examples on screen, avoiding full code packages to deepen understanding and prepare for live coding interviews.

  • Ratings2:54

    Learn why rating a long course too early misrepresents its value and why you should experience half the material before judging the Operations Research & Optimization Projects with Python course.

  • Follow This Roadmap for Success6:36

    Navigate the 27 hours of operations research with Python, optimization, and mathematical modeling. Explore real-world applications, metaheuristics like genetic algorithms and particle swarm optimization, dynamic programming, and machine learning integration.

  • About The Course5:02

    Learn mathematical models in Python for optimization through bite-size lessons, with coding in Jupyter notebooks, data handling, and result interpretation for all levels.

  • Installations2:00

    Install python from python's website (latest or 3.9) and set up with Anaconda Jupyter or Visual Studio Code; use pip to install libraries and consult the appendix PDF for help.

Requirements

  • Basic proficiency in Python programming is required.
  • Familiarity with fundamental concepts in operations management is beneficial but not required.
  • Basic mathematical skills, including algebra and introductory statistics.

Description

Welcome to this comprehensive course on Operations Research and Optimization, where you will master a range of optimization techniques essential for solving complex real-world problems. Whether you are starting out or an experienced professional looking to expand your knowledge in advanced algorithms, this course offers valuable insights and practical skills.

Throughout this course, we will cover key topics such as linear programming, discrete optimization, and stochastic processes. You will also explore sophisticated areas including machine learning-enhanced optimization algorithms, genetic algorithms, and multi-objective decision making. Each module is designed to gradually build your understanding, with practical examples and interactive exercises that directly apply to real-life scenarios.

We'll examine specific applications such as optimizing supply chains, dynamic programming in revenue management, and solving scheduling problems. You'll learn to use popular tools and libraries in Python, such as Gurob,SciPy, PuLP, Or-Tools equipping you with the skills to effectively implement these techniques in your projects.

Moreover, the course includes case studies from industries like manufacturing, healthcare, and logistics, providing context on how operations research is applied to optimize various operational aspects. By the end of this course, you will be equipped to analyze complex systems, design optimization strategies, and apply various optimization algorithms effectively.

Join me in this exploration to unlock the potential of operations research and optimization. You will finish this course not only with a deeper understanding of the theoretical aspects but also with the capability to apply this knowledge to enhance decision-making and efficiency in your professional life or academic pursuits.

This course is ideal for anyone who wishes to build a solid foundation in operations research, improve their analytical skills, and learn systematic approaches to tackle optimization problems.

Who this course is for:

  • This course is designed for professionals and students interested in the field of operations research and optimization.
  • Individuals aiming to enhance decision-making processes in industries such as manufacturing, logistics, and services.
  • Analysts and data scientists seeking to deepen their expertise in algorithmic problem-solving and operational efficiency.
  • Operations research students and professionals looking to enhance their Python skills with a focus on algorithmic solutions.

Report abuse