Loading…

Assessment of waste characteristics and their impact on GIS vehicle collection route optimization using ANN waste forecasts

•Coupling of ANN waste forecasts with GIS collection route optimization.•GIS routes with minimum travel distances are sensitive to waste composition.•Results of 36 scenarios yield changes in travel distance of up to 19.9%•Dual compartment trucks reduce travel distance but increase waste collection t...

Full description

Saved in:
Bibliographic Details
Published in:Waste management (Elmsford) 2019-04, Vol.88, p.118-130
Main Authors: Vu, Hoang Lan, Bolingbroke, Damien, Ng, Kelvin Tsun Wai, Fallah, Bahareh
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Coupling of ANN waste forecasts with GIS collection route optimization.•GIS routes with minimum travel distances are sensitive to waste composition.•Results of 36 scenarios yield changes in travel distance of up to 19.9%•Dual compartment trucks reduce travel distance but increase waste collection time. Combining an artificial neural network (ANN) waste prediction model with a geographic information system (GIS) waste collection route optimization, the paper shows how the compositional features of waste materials affect the optimized truck route time, distance, and air emissions. Using data from Austin, Texas, USA, a nonlinear autoregressive ANN model is used to predict the waste generation rate of the recycling and garbage streams for the year 2023 in four sub-areas of the city. This ANN model resulted in mean absolute percentage errors ranging from 10.92% to 16.51%. Modified compositions of the recycling and garbage streams are then used as inputs, along with the year 2023 generation rates, to create 6 modified and 3 non-modified scenarios that reflect possible future changes in waste composition. These waste stream scenarios are then used as input parameters to determine optimal waste collection routes with minimal travel distance in each of the four sub-areas using the GIS vehicle routing problem network analysis tool. Results of these 36 scenarios yield changes in travel distance of up to 19.9%, when compared to the non-modified composition. Further, dual compartment trucks were compared to single compartment trucks and found to save between 10.3 and 16.0% in travel distance and slightly reduce emissions but had a 15.7–19.8% increase in collection time. Results suggest temporal changes in waste composition and characteristics are important in GIS route optimization studies.
ISSN:0956-053X
1879-2456
DOI:10.1016/j.wasman.2019.03.037