Optimalizace svozu směsného komunálního odpadu v konkrétní části města Most

Abstract

This thesis focuses on the optimization of mixed municipal waste collection in a specific area of the city of Most. The primary objective is to assess the potential of data analysis and newly acquired waste container fill-level data for optimizing the waste collection system. For data collection, a network of IoT sensors utilizing infrared technology was employed to monitor container fill levels and provide insights into their filling patterns. The collected data were subsequently analyzed using Excel (primarily VBA) and RStudio, where statistical methods were applied to identify behavioral patterns and optimize waste collection routes. The implementation of VBA and RStudio scripts enabled partial automation of data analysis, eliminating manual errors and ensuring consistent and reproducible procedures for evaluating container fill-level trends, leading to more efficient planning of waste collection frequencies. The findings of this analysis contribute to a better understanding of the actual waste collection needs, resulting in cost reductions, time savings, and a more environmentally sustainable operation of waste collection vehicles.