Bio-inspired scheduling for dynamic job shops with flexible routing and sequence-dependent setups

Abstract

Flexible routing requires scheduling to be responsive and robust. Multi-agent systems have the potential to achieve robustness and provide a means for real-time planning and scheduling. The objective of this paper is to propose a multi-agent scheduling system with a good solution quality and robustness. The proposed multi-agent approach is designed for dynamic job shops with routing flexibility and sequence-dependent setup. A bio-inspired strategy based on division of labour in insect societies is presented for coordination among agents. The strategy is accomplished using a computational model which is composed of response threshold, response intention, and machine-centred reinforcement learning. The bio-inspired scheduling is compared with an agent-based approach and a dispatching rule-based approach. The experiments were performed using simulation and statistical analysis. Results show that the proposed bio-inspired scheduling model performs better than the other two methods on all eight common scheduling metrics.

Keywords:

• Dynamic job shop scheduling
• Flexible routing
• Sequence-dependent setup
• Multi-agent system coordination
• Division of labour
• Response threshold