Dr Neil Urquhart N.Urquhart@napier.ac.uk
Lecturer
Planning and optimising organisational travel plans using an evolutionary algorithm.
Urquhart, Neil B
Authors
Contributors
Cecilia Chio
Editor
Anthony Brabazon
Editor
Gianni A Caro
Editor
Rolf Drechsler
Editor
Muddassar Farooq
Editor
J�rn Grahl
Editor
Gary Greenfield
Editor
Christian Prins
Editor
Juan Romero
Editor
Giovanni Squillero
Editor
Abstract
Commuting to the workplace is a highly individualistic experience, especially where the private car is the chosen mode of transport. The costs of using cars with low occupancy rates are significant in environmental terms as well as requiring the provision of parking space at the workplace. This paper examines the use of an Evolutionary Algorithm based problem solver to construct travel plans for three sites with 248,404 and 520 employees respectively at each site. Results presented suggest that a significant saving in overall distance travelled and parking spaces required is possible. The algorithm employed takes into account both hard constraints and soft constraints (such as work patterns and journey flexibility).
Citation
Urquhart, N. B. (2011, April). Planning and optimising organisational travel plans using an evolutionary algorithm. Presented at European Conference on the Applications of Evolutionary Computation
Conference Name | European Conference on the Applications of Evolutionary Computation |
---|---|
Start Date | Apr 27, 2011 |
End Date | Apr 30, 2011 |
Publication Date | 2011 |
Deposit Date | Jun 27, 2011 |
Publicly Available Date | May 16, 2017 |
Peer Reviewed | Peer Reviewed |
Volume | 6625 |
Pages | 464-470 |
Series Title | Lecture Notes in Computer Science |
Series Number | 6625 |
Series ISSN | 0302-9743 |
Book Title | Applications of Evolutionary Computation |
ISBN | 9783642205194 |
DOI | https://doi.org/10.1007/978-3-642-20520-0_47 |
Keywords | Evolutionary algorithm; transport; commuting; journey planning; private car; |
Public URL | http://researchrepository.napier.ac.uk/id/eprint/4549 |
Publisher URL | http://dx.doi.org/10.1007/978-3-642-20520-0_47 |
Contract Date | May 16, 2017 |
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