2022 IEEE CASE Conference Talk


As industry rapidly shifts towards mass personalisation, the need for a decentralised multi-agent system capable of dynamic flexible job shop scheduling (FJSP) is evident. Traditional heuristic and meta-heuristic scheduling methods cannot achieve satisfactory results and have limited application to static environments. Recent Reinforcement Learning (RL) approaches that consider dynamic FJSP, lack flexibility and autonomy as they use a single-agent centralised model, assuming global observability. As such, we propose a Multi-Agent Reinforcement Learning (MARL) system for scheduling dynamically arriving assembly jobs in a robot assembly cell. We applied a Double DQN-based algorithm and proposed a generalised observation, action and reward design for the dynamic FJSP setting. Using a centralised training phase, each agent (i.e., robot) in the assembly cell executes decentralised scheduling decisions based on local observations. Our solution demonstrated improved performance against rule-based heuristic methods, for optimising makespan. We also reported the impact of different observation sizes of each agent on optimisation performance.

20 Aug, 2022 12:00 AM — 24 Aug, 2022 12:00 AM
Sheraton Mexico City Maria Isabel Hotel
Av. Paseo de la Reforma 325, Ciudad de México, Cuauhtémoc 06500
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Dazzle Johnson
Dazzle Johnson
PhD Student

My research interests include robot collaboration, multi-agent systems and reinforcement learning.