Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Large-scale models are currently used for the simulation, analysis and control of real systems, whether technical, biological, social or economic. In multi-agent simulations of virtual economies, it is important to schedule a large number of agents across the cities involved, in order to establish a functional supply chain network for industrial production. This study describes an experimental evaluation of path-planning approaches in the field of multi-agent modelling and simulation, applied to a large-scale setting. The experimental comparison is based on a model in which agents represent economic entities and can participate in mutual interactions. For the purposes of experiment, the model is scaled to various degrees of complexity in terms of the numbers of agents and transportation nodes. Various numbers of agents are used to explore the way in which the model's complexity influences the runtime of the path-planning task. The results indicate that there are significant differences between the runtime performances associated with single approaches, for differing levels of system complexity and model sizes. The study reveals that the appropriate sharing of shortest path information can significantly improve path-planning activities. Hence, this work extends current research in the field of path-planning for multi-agent simulations by conducting an experimental performance analysis of five distinct path-planning approaches and a statistical evaluation of the results. This statistical evaluation contrasts with performance analyses conducted on the basis of 'Big O' notation for algorithmic complexity, which describes the limiting behaviour of the algorithm and gives only a rough performance estimate.