Optimizing demand-responsive IoT-based waste collection services: a two-step clustering technique

Abstract

The Internet of Things (IoT) can bring radical advancements in the domain of waste collection, as it enables the organization of demand-responsive schedules which leads to higher efficiency operations. One major challenge in the deployment of demand-responsive schedules, nevertheless, is the uncertainty they bring in the planning of resources as they follow the daily waste demand. This is undesirable in real-life operations as it makes it difficult to reserve resources and ensure the stability of operational processes. Therefore, waste collection scheduling approaches need to be devised that are not only demand-responsive but also supply-friendly. In this paper, we present a solution approach for the waste collection vehicle routing problem in an IoT context (IoT-WCVRP) that focuses on these requirements. We demonstrate its applicability through a case study of Rotterdam in The Netherlands, where real-life household waste data are used and the observed waste collection operations in the city are compared against the optimized outcomes of the model. The application results show that our IoT-WCVRP approach achieves the stated demand and supply trade-off, increases the vehicle utilization rates by 5%, and reduces emissions and travelled kilometres by 6% and 8% respectively.