  Optimisation of fleets of commercial vehicles with regards scheduling tasks
from various locations to vehicles can result in considerably lower fleet
traversal times. This has significant benefits including reduced expenses for
the company and more importantly, a reduction in the degree of road use and
hence vehicular emissions. Exact optimisation methods fail to scale to real
commercial problem instances, thus meta-heuristics are more suitable. Ant
Colony Optimisation (ACO) generally provides good solutions on small to medium
problem sizes. However, commercial fleet optimisation problems are typically
large and complex, in which ACO fails to scale well. Partial-ACO is a new ACO
variant designed to scale to larger problem instances. Therefore this paper
investigates the application of Partial-ACO on the problem of fleet
optimisation, demonstrating the capacity of Partial-ACO to successfully scale
to larger problems. Indeed, for real-world fleet optimisation problems supplied
by a Birmingham based company with up to 298 jobs and 32 vehicles, Partial-ACO
can improve upon their fleet traversal times by over 44%. Moreover, Partial-ACO
demonstrates its ability to scale with considerably improved results over
standard ACO and competitive results against a Genetic Algorithm.
