Workers of the Argentine ant, Iridomyrmex humilis, are known to be capable to find efficiently the shortest route from their nest to a food source. Their approach is based on a simple pheromone trail-laying and following behaviour accessing only local information. In this note we explore the modelling and analysis of foraging ants in Bio-PEPA [8,6]. The simple case study concerns ants that need to cross a bridge with two branches of different length to reach food and carry the food home and is based on empirical data described by Goss and Deneubourg et al. [13,10]. We explore the conditions for which the shortest path emerges as the preferred one by the ants. The analysis is based on stochastic simulation and fluid flow analysis. The behaviour of ant colonies has inspired the development of an interesting class of optimisation algorithms ranging from alternative shortest path algorithms to new scheduling and routing algorithms, algorithms to solve set partition problems and for distributed information retrieval. Process algebraic fluid flow analysis may be an important additional technique to the analysis of such algorithms in a computationally efficient way.