Can ant algorithms make automated guided vehicle system more intelligent?

In manufacturing environment, an automated guided vehicle (AGV) system is composed of a set of driver-less vehicles that transport goods and materials between distinct workstations and storage locations of shops. In soft computing area, ant algorithms are a series of population-based approaches inspired by various behaviors of real ant colonies. During the last two decades, ant algorithms have achieved a great success in solving many combinatorial optimization problems. In this article we make an attempt to study the feasibility of applying ant algorithms to different problems encountered in AGV system design and control. By making use of ant algorithms' strengths, we hope to provide the readers with alternative options for solving conventional AGV system design and control problems, as well as to point out some new directions for AGV system research.