Optimal Sea Route Optimization
Developing two custom algorithms to define optimal maritime routes for CMA-CGM — minimizing costs, time, and carbon footprint on the path to carbon neutrality by 2050.
Business Context
CMA-CGM, a global leader in maritime transport, is targeting carbon neutrality by 2050. The challenge was to optimize their maritime routes to reduce both costs and ecological footprint.
Strategic Problem
How to define the optimal sea routes for CMA-CGM to minimize costs, transit time, and carbon emissions using custom optimization algorithms?
Data Sources
Route distance matrices, fuel consumption models, carbon emission factors, transhipment cost data, port throughput metrics, and CO2 emission datasets.
Methodology
Developed two custom algorithms in 3 weeks: (1) a variant of Dijkstra's algorithm finding the shortest path considering 4 key variables (time, cost, transhipment, CO2), and (2) a Travelling Salesman Problem-inspired algorithm optimizing CMA-CGM's full maritime lines end-to-end.
Key Results
Delivered two functional optimization algorithms that demonstrably reduce CMA-CGM's carbon footprint while accounting for time, transhipment costs, and emissions constraints.
Business Impact
Directly applicable to maritime logistics decarbonization. Demonstrated algorithmic problem-solving at scale for one of the world's largest shipping companies.