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Applied Optimization Modeling: Pyomo, GAMS & Mosel
Highest Rated
Rating: 5.0 out of 5(188 ratings)
1,118 students

Applied Optimization Modeling: Pyomo, GAMS & Mosel

Master Industry Tools for Energy Systems, Emissions Forecasting & Resource Planning
Highest Rated
Created byAlgorithmic Economist, PhD
Last updated 6/2026
English

What you'll learn

  • Model and solve optimization problems using three industry-leading tools: GAMS, Pyomo, and Mosel
  • Implement real-world case studies including power substation optimization
  • Master fundamental concepts: optimality, infeasibility, unboundedness, duality, and slackness
  • Create MILP (Mixed Integer Linear Programming) models for complex discrete decisions
  • Integrate Excel data with optimization models for seamless workflow
  • Use procedural and functional coding approaches in Mosel
  • Compare and choose the right tool (Pyomo, GAMS, or Mosel) for specific problems

Course content

6 sections • 29 lectures • 2h 21m total length

  • Introduction4:17

    Introduction : what is optimization. Download the attached material below.

  • Additional Case Studies0:03

    Important

Requirements

  • No programming experience required
  • No optimization background needed
  • Familiarity with Excel helpful but not required
  • Software installation instructions provided for all tools
  • Just need a computer and willingness to learn

Description

WHO I AM: I hold a PhD in Energy from Imperial College London. I teach practical, real-world data science specifically for the energy sector.


REGULAR ENHANCEMENTS: This course is reviewed periodically with updates to reflect the modern energy market.


STUDENT BONUS: Note: Students who enroll in this course will receive access to the Energy Data Scientist community.


What You'll Learn:

  • How to model and solve real-world optimization problems using GAMS, Pyomo, and Mosel

  • How to handle large-scale optimization problems with multiple constraints and variables

  • How to master fundamental concepts: optimality, infeasibility, unboundedness, duality, and slackness

  • How to use Mosel's advanced features including MILP models, Excel integration, and multidimensional variables

  • How to translate real industrial problems into mathematical optimization models

  • How to choose the right tool (Pyomo, GAMS, or Mosel) for specific optimization challenges


Perfect For:

  • Energy economists and planners optimizing grid operations and resource allocation

  • Operations research analysts solving logistics and supply chain problems

  • Environmental consultants forecasting emissions and planning sustainability strategies

  • Graduate students in engineering, economics, or operations research

  • Data scientists adding optimization capabilities to their toolkit

  • Financial analysts optimizing portfolios and risk management

  • Industrial engineers improving manufacturing and production systems

  • Anyone needing to solve complex decision-making problems quantitatively


Why This Matters:

Optimization drives trillion-dollar decisions in energy markets, supply chains, and climate planning. As industries race toward net-zero, professionals who can model complex systems and forecast emissions are commanding premium salaries. Whether optimizing renewable energy integration, minimizing transportation costs, or forecasting CO₂ trajectories for policy planning, these skills are non-negotiable for data-driven decision making. GAMS, Pyomo, and Mosel are the industry standards used by energy companies, consulting firms, and research institutions worldwide. Master the tools that power everything from Google's data center optimization to national grid planning. These capabilities open doors to senior analyst and optimization engineer roles paying $150,000-280,000+ in energy, logistics, finance, and environmental consulting.

Who this course is for:

  • Energy Economists & Planners optimizing grid operations and renewable integration
  • Operations Research Analysts solving complex logistics and resource allocation problems
  • Environmental Consultants forecasting emissions for sustainability planning
  • Supply Chain Managers minimizing costs and improving distribution networks
  • Graduate Students & Researchers in engineering, economics, or operations research
  • Data Scientists adding optimization modeling to their analytical toolkit
  • Financial Analysts working on portfolio optimization and risk management
  • Anyone solving decision problems requiring mathematical optimization

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