Scalable Scheduling of Semiconductor Packaging Facilities Using Deep Reinforcement Learning

• Published in: IEEE transactions on cybernetics Vol. 53; no. 6; pp. 3518 - 3531
• Main Authors: Park, In-Beom, Park, Jonghun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace electronics; Artificial neural networks; Deep deterministic policy gradient (DDPG); Deep learning; deep reinforcement learning (DRL); flexible job shop scheduling; Heuristic methods; Job shop scheduling; Metaheuristics; Packaging; Processor scheduling; Production; Representations; Scheduling; semiconductor manufacturing system; semiconductor packaging

Reinforcement learning (RL) has emerged as a promising approach for scheduling semiconductor operations. Yet, it is still challenging to solve large-scale scheduling problems based on an RL method since learning complexity grows fast as the size of shop floor increases. This challenge becomes more a...