Space-planning by ant colony optimisation

In this paper a new heuristic combinatorial optimisation algorithm, called ant colony optimisation (ACO), is applied to the space-planning problem of determining an optimal assignment of activities (administrative functions/personnel) to locations (offices) for an organisation housed in an office block. This problem arises, for example, when a commercial organisation wishes to reduce (i.e, minimise) the amount of physical movement within its building(s) (e.g. flow of paperwork and personnel) in an attempt to improve operational efficiency. The ACO algorithm is motivated by analogy with natural phenomena, in particular, the ability of a colony of ants to 'optimise' their collective endeavours. In this paper the biological background for ACO is explained and its computational implementation is presented in a space-planning context. The particular implementation of ACO makes use of a tabu search (TS) local improvement phase to give a computationally enhanced algorithm (ACOTS). Two examples are then used to show that ACOTS is a useful and viable optimisation technique to obtain layout designs for large-scale space-planning problems.