Optimizing a recycling system
How I redesigned waste collection for one of Chile’s largest universities using analytics, AHP and Python.
Overview
While working for the Sustainability Directorate at Pontificia Universidad Católica de Chile, I led the redesign of the inorganic recycling system for the San Joaquín campus. I combined field data, decision-making models and route-optimization algorithms to eliminate overflows, reduce collection inefficiencies and create a scalable recycling infrastructure.
Challenges
The recycling system was failing: containers overflowed, collection routes were inefficient, and waste was being mishandled. The university had no data-driven way to decide how many bins were needed, where to place them, or how to collect them efficiently.
My Role
- Collecting and cleaning operational data
- Designing the sizing and location model
- Running multi-criteria decision analysis (AHP)
- Optimizing collection routes in Python
- Producing the technical proposal for implementation
Methodology
1. Field & expert data collection
I gathered qualitative and quantitative data through on-site measurements and expert interviews to understand waste generation, container usage and collection constraints.
2. System sizing
I applied Zafra-Mejía’s methodology to calculate the required number and capacity of recycling points across campus.
3. Location optimization
I used the Analytic Hierarchy Process (AHP) to rank and select optimal locations based on accessibility, waste generation, and operational constraints.
4. Route optimization
I implemented routing algorithms in Python to minimize travel distance and collection time, reducing operational costs and overflow risk.
Results
