Clustered vehicle routing problem for waste collection with smart operational management approaches

Abstract

Waste collection is one of the essential tasks in a smart city. The Internet of Things (IoT) is a promising technology that offers potential solutions for transforming traditional systems. An IoT-based smart bin is a modern technology that offers real-time fill level information to a cleaning authority. However, high uncertainty associated with the smart bin's fill levels and improper operation hinder efficient waste collection. In order to tackle the uncertainty in a smart bin and improve the waste collection operation, the IoT sensor's usage must be combined with optimization procedures. The present work introduced two operational management approaches to define dynamic optimal routes and combined ant colony optimization with a k-means clustering algorithm to solve the clustered vehicle routing problem for waste collection on a large scale. Operational management approaches reflect practical constraints when using IoT-based smart bins. A hybrid metaheuristic is proposed and performed with these approaches thereby showing the potential of building a smart waste collection system.