| dc.description.abstract | The County Government of Mombasa (CGM) faces a major challenge in solid waste
management (SWM), which involves collecting and removing waste. The study aimed
to improve the waste transportation and logistics operations from collection points to
final disposal by computing an algorithm for the optimal waste collection route in
Mombasa. The ultimate goal of this study is to develop a model that optimizes the
waste collection system using machine learning and geographical information system
(GIS) technologies. The study aimed to determine the optimal waste collection route
using ant colony, integer linear programming, and simulated annealing algorithms.
These algorithms were used to analyze the data collected on the waste collection
process, which included locations of transfer stations, time taken, distance, and cost for
fuel. The data was first cleaned and tested for distribution, and the optimal factors were
then identified. The supervised machine learning process was then applied to identify
the relationships between the variables, make predictions, and find the optimization
route. Finally, the integration of geographical information system (GIS) process was
used to determine the final optimal route for the waste collection process. The results
showed that the use of ant colony, integer linear programming, and simulated
annealing algorithms was effective in finding the optimal waste collection route,
resulting in a more efficient, cost-effective, and sustainable waste management system.
This study is crucial and greatly benefit the department responsible for SWM in the
CGM by improving the efficiency and effectiveness of waste collection. It primarily use
quantitative research methods and intended to evaluate the current waste collection
network. The study developed a map of the existing waste collection network using
quantum geographical information system (QGIS) software and use a supervised
machine learning model to find the best solution to the route optimization problem,
determining the shortest path with the minimum cost | en_US |
