A heuristic for the deployment of collecting routes for urban recycle stations (eco-points)

TL;DR

This paper introduces a heuristic method for optimizing the deployment and routing of eco-point containers in urban waste collection, aiming to improve environmental and operational efficiency.

Contribution

It presents a new mathematical model combining Bin Packing and Vehicle Routing schemes, along with a heuristic algorithm for container deployment in urban waste collection.

Findings

Validated on Sioux Falls benchmark network

Effective in real case study scenarios

Improves waste collection efficiency

Abstract

The rapid and constant increase in urban population has led to a drastic rise in urban solid waste production with worrying consequences for the environment and society. In many cities, an efficient waste management combined with a suitable design of vehicle routes (VR) can lead to benefits in the environmental, economic, and social impacts. The general population is becoming increasingly aware of the need for the separation of the various categories of municipal solid waste. The numerous materials collected include glass, PET or batteries, and electric components, which are sorted at the eco-points. The management of eco-points gives rise to several problems that can be formulated analytically. The location and number of eco-point containers, the determination of the fleet size for picking up the collected waste, and the design of itineraries are all intertwined, and present computationally difficult problems, and therefore must be solved in a sequential way. In this paper, a mathematical model has been formulated, based on the Bin Packing (BP) and VR schemes, for the deployment of routes of mobile containers in the selective collection of urban solid waste. A heuristic algorithm has also been developed, which considers two different configurations of the containers to solve the proposed mathematical programming model. The results obtained from the numerical simulations show the validation of the proposed methodology carried out for the benchmark of the Sioux Falls network and the specific real case study.