Incorporating GA-Based Optimization into a Multi-Agent Architecture for FMS Scheduling

Scheduling for flexible manufacturing systems (FMS) poses the challenge of optimizing the generated schedule while exhibiting flexibility to environmental dynamics. While the agent-based paradigm has been shown to tackle the inherent complexity of the problem and exhibit the required flexibility, it hinders the global optimization due to its decentralized structure. In this paper, genetic algorithms (GA) are incorporated into an agent-based scheduling architecture to overcome this shortcoming of agents and to achieve the required combination of flexibility with efficiency. Based on GA, individual agents optimize the schedule from their local view. Through the cooperation among the individual agents, a near-optimal schedule under consideration of the different optimization objectives is attained. Test results prove that the proposed integration yields near-optimal schedules with low computational complexity.