Pollution from vehicles in congested cities is becoming increasing concern throughout the world. Indeed, many busy cities have introduced clean air policies such as congestion charges to reduce air pollution from road traffic. One contributor to traffic pollution is fleets of vehicles being used to perform scheduled tasks such as deliveries or maintenance. This paper will demonstrate how heuristic optimisation can better schedule the allocation of tasks to vehicles over longer term periods such that considerable reductions in vehicle usage can be achieved. Genetic Algorithms and Ant Colony Optimisation approaches will be compared as to their respective ability to reduce long term vehicle usage for a Birmingham based maintenance company which has a fleet of vans. Indeed, this paper demonstrates that with longer range optimisation as much as a 45% reduction in vehicle usage and hence emissions can be achieved with the associated benefit of reduced fuel costs.
|Title of host publication||Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2|
|Editors||Yaxin Bi, Rahul Bhatia, Supriya Kapoor|
|Number of pages||20|
|Publication status||E-pub ahead of print - 24 Aug 2019|
|Name||Advances in Intelligent Systems and Computing|
Bibliographical noteFunding: System Analytics for Innovation project, part-funded by the European Regional Development Fund (ERDF).
- Emission reduction
- Heuristic optimisation
- Vehicle Routing
Chitty, D. M., Parmar, R., & Lewis, P. R. (2019). Improving Urban Air Quality Through Long-Term Optimisation of Vehicle Fleets. In Y. Bi, R. Bhatia, & S. Kapoor (Eds.), Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2 (Vol. 1038, pp. 70-89). (Advances in Intelligent Systems and Computing; Vol. 1038). Springer. https://doi.org/10.1007/978-3-030-29513-4_6