Smart School Recycling: An Agent-Based NetLogo Study of Recycling Efficiency at Santo Domingo School

Schools generate paper, plastic, cardboard, and packaging waste. Recycling collection may depend on bin availability, student population, and waste generation. This project will use NetLogo to investigate how these factors affect recyclable collection and remaining waste in a simulated Santo Domingo School, without assuming existing problems unsupported by local evidence.

The purpose is to compare recycling strategies through agent-based modeling. Students, waste items, and bins will interact through programmed rules. The team predicts that additional recycling bins will improve collection when other conditions remain constant. Results will describe model performance rather than prove effectiveness in the school without further testing.

The team will research recycling and, with permission, review bin locations or anonymous audit information. No personal data will be collected, and students will avoid unsafe waste. In the model, students move randomly and generate waste, while matching bins automatically collect nearby items. Sliders and monitors control settings and measurements.

After verifying accuracy, testing will begin with 20 students, three recycling bins, three trash bins, a 4% waste-generation probability per student per tick, and 500 simulation steps. One factor will change at a time before combined scenarios. Each scenario will run at least five times with recorded seeds and results.

Recycling efficiency will equal recyclable items collected divided by recyclable items generated, multiplied by 100. Runs generating no recyclables will be marked not applicable. The team will compare averages, variation, and remaining waste, then prepare graphs and a Supercomputing Challenge report explaining findings, limitations, and recommendations for further school-based investigation.