Abstract:
This paper applies a meta-heuristic based Ant
Colony Optimization (ACO) technique for simultaneous task
allocation and path planning of Automated Guided Vehicles
(AGV) in material handling. ACO algorithm allocates tasks to
AGVs based on collision free path obtained by a proposed path
and motion planning algorithm. The validity of this approach is
investigated by applying it to different task and AGV
combinations which have different initial settings. For small
combinations, i.e. small number of tasks and vehicles, the quality
of the ACO solution is compared against the optimal results
obtained from exhaustive search mechanism. This approach has
shown near optimal results. For larger combinations, ACO
solutions are compared with Simulated Annealing algorithm
which is another commonly used meta-heuristic approach. The
results show that ACO solutions have slightly better performance
than that of Simulated Annealing algorithm.
