Sustainable logistic
Education
Optimisation of F1 logistics
In the Sustainable Logistics and Operations course, we tackled one of the most polluting championships in the world, aiming to optimize it to reduce its CO2 emissions. We adopted a pragmatic approach, considering all contractual and seasonal constraints faced by the current calendar.
First, we modeled the season by creating data on the distances between each Grand Prix. We then calculated the impact of each journey, considering different modes of transportation—airplanes, trucks, and cargo ships—while factoring in infeasible routes. Additionally, we calculated the travel time for each mode of transport.
With this data in hand, we performed an optimization using the Python library Google OR-Tools, arriving at the following results:
Current Calendar
Optimal Calendar
%
Reduction of C02
%
Reduction of km
Millions of cost saving
My Takeaways
Optimization Techniques
Utilized the Google OR-Tools library to perform complex optimizations, finding the most sustainable travel routes.
Pragmatic Approach
Integrated real-world constraints such as contractual obligations and seasonal factors into our model.
Provided actionable insights and recommendations for making the championship more sustainable without compromising its operational integrity.
Data Modeling
Developed a comprehensive model of the championship season, including distances and travel modes.
Calculated the CO2 impact and travel time for each mode of transport, considering all relevant constraints.