The research group of the faculty of Electronics and Automation in
For the sake of the cooperative scheduling method of multi-load AGVs based on multi-agent theory, we presented the distributed optimization problem for the cooperative scheduling, in which the transfer crossing over several zones was being mapped into the transfers at each of these zones. And single-agent scheduling was performed within the respective zone, minimizing the difference between the arrived time of AGVs for adjacent zones at transfer point(TP)s.
We divided the distributed optimization problem into single-agent one using Lagrange multipliers and made the sum of optimal solutions for all of agents to the optimal solution of the overall system.
We developed a new algorithm to perform a single-agent scheduling using multi-offspring genetic algorithm as well as the cooperative multi-agent scheduling and found to reduce the transfer time by 30% over non-cooperative one through the simulation experiment.
Our work has been published in "Engineering Applications of Artificial Intelligence"(123(2023) 106229) in 2023 under the title of "Multi-agent based scheduling method for tandem automated guided vehicle systems" (https://doi.org/10.1016/j.engappai.2023.106229).
