| This paper proposes a novel approach that integrates hierarchical clustering (HC) into reinforcement learning algorithms to address the simultaneous resolution of hybrid assembly line balancing (ALB-1) and assembly sequence planning (ASP). The proposed approach attempts to capture implicit constraints, derived from accumulated experiences and industry-specific knowledge, enhancing the adaptability of solutions. The inclusion of the clustering algorithm enhances the decision-making process of the reinforcement learning agent through the introduction of a problem-specific similarity reward. To evaluate the effectiveness of the approach, three adapted methods are implemented and tested: QL-HC, SARSA-HC, and SARSA without HC. The experimental results demonstrate the superior performance of our novel approach compared to traditional techniques, with SARSA-HC exhibiting particularly impressive results with 82% of similarity to the expert's sequence. This approach offers increased flexibility, adaptability, and the capacity to incorporate expert knowledge and lessons learned from prior assembly line design experiences and proves to be a valuable tool for enhancing efficiency and reducing time-to-market in manufacturing settings. |
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