@inproceedings { , title = {Planning and optimising organisational travel plans using an evolutionary algorithm.}, 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).}, conference = {European Conference on the Applications of Evolutionary Computation}, doi = {10.1007/978-3-642-20520-0\_47}, isbn = {9783642205194}, note = {School: iidi}, organization = {Torino, Italy}, pages = {464-470}, publicationstatus = {Published}, url = {http://researchrepository.napier.ac.uk/id/eprint/4549}, volume = {6625}, keyword = {006.3 Artificial intelligence, QA76 Computer software, Optimisation and learning, AI and Technologies, Evolutionary algorithm, transport, commuting, journey planning, private car;}, year = {2024}, author = {Urquhart, Neil B} editor = {Chio, Cecilia and Brabazon, Anthony and Caro, Gianni A and Drechsler, Rolf and Farooq, Muddassar and Grahl, Jörn and Greenfield, Gary and Prins, Christian and Romero, Juan and Squillero, Giovanni} }