Multi-Agent Deep Q-Network with Layer-based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturing
This addresses operational efficiency for smart manufacturing systems, but it is incremental as it builds on existing multi-agent deep Q-network methods.
The paper tackles autonomous internal logistics vehicle scheduling in smart manufacturing by proposing a multi-agent deep Q-network with a layer-based communication channel, resulting in minimized job tardiness, reduced tardy jobs, and lower energy consumption compared to nine scheduling heuristics.
In smart manufacturing, scheduling autonomous internal logistic vehicles is crucial for optimizing operational efficiency. This paper proposes a multi-agent deep Q-network (MADQN) with a layer-based communication channel (LBCC) to address this challenge. The main goals are to minimize total job tardiness, reduce the number of tardy jobs, and lower vehicle energy consumption. The method is evaluated against nine well-known scheduling heuristics, demonstrating its effectiveness in handling dynamic job shop behaviors like job arrivals and workstation unavailabilities. The approach also proves scalable, maintaining performance across different layouts and larger problem instances, highlighting the robustness and adaptability of MADQN with LBCC in smart manufacturing.