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Deep Reinforcement Learning for Dynamic Job-Shop Scheduling in High-Utilization Systems

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Deep Reinforcement Learning for Dynamic Job-Shop Scheduling in High-Utilization Systems.

Rel. Paolo Brandimarte, Edoardo Fadda. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2024

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Abstract:

This thesis presents the development and evaluation of a Double Deep Q-learning Network (DDQN) agent for addressing the Dynamic Job-Shop Scheduling Problem (DJSP) in high-utilization systems. The DDQN agent is designed to optimize job scheduling dynamically, taking into account stochastic arrivals and stochastic jobs attributes. By employing separate networks for action selection and evaluation, the DDQN mitigates overestimation bias, enhancing the stability and accuracy of the learning process. Preliminary results indicate that the DDQN agent performs well in highly utilized systems, demonstrating significant promise in optimizing scheduling efficiency, though its performance is comparable to some well-tailored traditional heuristics. However, its performance in less utilized systems remains less effective, suggesting room for further refinement. The findings highlight the potential of reinforcement learning techniques in complex, dynamic industrial environments and open the way for future advancements in adaptive scheduling solutions.

Relators: Paolo Brandimarte, Edoardo Fadda
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 78
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Matematica
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/31608
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