Approximate dynamic programming for automated vacuum waste collection systems |
| |
| Affiliation: | 1. INSPIRES Research Institute, Universitat de Lleida, Lleida, Spain;2. Artificial Intelligence Research Institute (IIIA, CSIC), Bellaterra, Spain;3. Chemical and Biological Engineering Department, University of British Columbia, Vancouver, Canada;1. Department of Psychiatry, University of Arizona College of Medicine, Tucson, AZ, USA;2. Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA;1. Institute for Mathematics, Kassel University, Germany;2. Department of Computing Science, University of Alberta, Canada |
| |
| Abstract: | The collection and treatment of waste poses a major challenge to modern urban planning, particularly to smart cities. To cope with this problem, a cost-effective alternative to conventional methods is the use of Automated Vacuum Waste Collection (AVWC) systems, using 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. This paper describes and empirically evaluates a novel approach to defining daily operation plans for AVWC systems to improve quality of service, and reduce energy consumption, which represents about 60% of the total operation cost. We model a daily AVWC operation as a Markov decision process, and use Approximate Dynamic Programming techniques (ADP) to obtain optimal operation plans. The experiments, comparing our approach with the current approach implemented in some real-world AVWC systems, show that ADP techniques significantly improve the quality of AVWC operation plans. |
| |
| Keywords: | Urban waste Optimization Learning AVWCS Vacuum waste collection Smart cities |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
