DocumentCode :
1904000
Title :
Optimizing Energy Consumption in Automated Vacuum Waste Collection Systems
Author :
Bejar, R. ; Fernandez, Camino ; Manya, F. ; Mateu, C. ; Sole-Mauri, F.
Author_Institution :
Dept. of Comput. Sci., Univ. de Lleida, Lleida, Spain
Volume :
1
fYear :
2012
fDate :
7-9 Nov. 2012
Firstpage :
291
Lastpage :
298
Abstract :
Automated vacuum waste collection (AVWC) uses air suction on a closed network of underground pipes to transport waste from the drop off points scattered throughout the city to a central collection point, reducing greenhouse gas emissions and the inconveniences of conventional methods (odors, noise). Since a significant part of the cost of operating AVWC systems is energy consumption, we have started a project, together with a company that builds and installs such systems, with the aim of applying constraint programming technology to schedule the daily emptying sequences of the drop off points in such a way that energy consumption is minimized. In this paper we describe how the problem of deciding the drop off points that should be emptied at a given time can be modeled as a constraint integer programming (CIP) problem. Moreover, we report on experiments using real data from AVWC systems installed in different cities that provide empirical evidence that CIP offers a suitable technology for reducing energy consumption in AVWC.
Keywords :
air pollution; constraint handling; energy conservation; energy consumption; integer programming; minimisation; pipes; vacuum apparatus; waste disposal; waste handling; AVWC systems; CIP problem; automated vacuum waste collection systems; central collection point; constraint integer programming problem; drop off point daily emptying sequences; energy consumption minimization; energy consumption optimization; greenhouse gas emission reduction; underground pipes; waste transportation; Atmospheric modeling; Cities and towns; Energy consumption; Junctions; Linear programming; Programming; Valves;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on
Conference_Location :
Athens
ISSN :
1082-3409
Print_ISBN :
978-1-4799-0227-9
Type :
conf
DOI :
10.1109/ICTAI.2012.47
Filename :
6495059
Link To Document :
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