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Keep shoppingModelling and Optimization of Processes
1,448 students
Modelling and Optimization of Processes
Learn Practical Techniques to Model and Optimize Processes Using Numerical Methods, Python and Google Sheets
Created byEuan Russano
Last updated 2/2025
English
What you'll learn
- Build mathematical models for engineering processes, defining variables, constraints, and objective functions.
- Apply optimization techniques to solve linear and nonlinear problems in real-world scenarios.
- Use Google Sheets and Python to model and solve optimization problems, including production planning and resource allocation.
- Formulate and solve multi-objective optimization problems, balancing trade-offs between conflicting goals in engineering processes.
- Identify and handle constraints effectively in optimization problems using practical examples.
- Analyze optimization results to make data-driven decisions for process improvement and cost reduction.
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Course content
5 sections • 22 lectures • 1h 28m total length
Requirements
- Basic understanding of algebra and calculus – A foundational knowledge of equations, functions, and derivatives will help in understanding mathematical models and optimization techniques.
- Familiarity with engineering processes – While not mandatory, having some exposure to real-world engineering systems or workflows will enhance the learning experience.
- Access to a computer with internet – Learners will need a computer to practice solving optimization problems using Google Sheets and Python.
- Very basic programming experience – Some prior exposure to any programming language will help learners follow Python-based optimization examples more easily.
Description
Master the art and science of process modeling and optimization in this in-depth, hands-on course! Designed for engineers, scientists, and professionals, this course provides you with the essential tools and techniques to analyze, optimize, and improve real-world processes. By combining mathematical concepts with practical applications, you'll learn how to make informed, data-driven decisions to enhance efficiency and solve complex problems in any domain.
The course begins with a strong foundation in Linear Programming (LP), where you'll explore various solution methods, including Grid Search, Graphical Method, and the widely used Simplex Algorithm. We’ll then delve into Integer Programming and tackle Network-based Problems, equipping you with the skills to handle scenarios like resource allocation, routing, and scheduling. To solve these complex problems, you'll learn the Branch and Bound Algorithm, a powerful approach for achieving optimal solutions.
We’ll also cover Non-linear Problems, introducing techniques to optimize processes using calculus-based solutions. These concepts are vital for understanding and solving real-world problems that go beyond linear assumptions.
The course places a strong emphasis on practicality by integrating applications with Python and Google Sheets. You’ll learn how to implement these optimization techniques step by step, applying them to real-world scenarios such as production planning, resource allocation, and cost minimization. By the end of the course, you’ll have a complete toolkit of optimization strategies and the ability to implement them with modern, accessible tools.
Whether you're new to optimization or looking to strengthen your skills, this course will guide you every step of the way. No advanced programming knowledge is required—just a passion for problem-solving and process improvement. Enroll today and take the first step toward mastering process optimization!
Who this course is for:
- Engineering students (chemical, mechanical, civil, industrial, and related fields) who want to learn practical skills in modeling and optimizing processes.
- Process engineers and analysts looking to improve efficiency, reduce costs, and make data-driven decisions.
- Operations managers responsible for optimizing workflows and resource allocation in their organizations.
- Data enthusiasts and problem solvers interested in learning how to apply mathematical models and optimization techniques to real-world challenges.
- Beginners in optimization who have very basic programming experience and want to learn practical applications of Google Sheets and Python for solving optimization problems.