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

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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.
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Keywords: Division of labour; Dynamic job shop scheduling; Flexible routing; Multi-agent system coordination; Response threshold; Sequence-dependent setup

Document Type: Research Article

Affiliations: Department of Industrial and Systems Engineering, North Carolina Agriculture and Technical State University, Greensboro, NC 27411, USA

Publication date: 2006-11-15

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