<![CDATA[This study seeks to address the complicated optimization challenge inherent in cut order planning (COP) in the clothing manufacturing business, emphasising fabric consumption, computational economy, and production accuracy. Three optimization approaches were compared: adaptive heuristic scoring optimizer (AHOPS), hybrid metaheuristic optimization with simulated annealing (HIMOSA), and gradient-based penalty-driven (GBPD). The results show that the GBPD method achieved the highest fabric utilization (87.13%), the fewest amount of fabric layers (12), and the maximum computational efficiency (0.022 seconds), significantly outperforming both conventional methods and alternative advanced approaches. AHOPS and HIMOSA, on the other hand, required more layers (15) and produced lower fabric utilization (around 69.70%), with HIMOSA demonstrating noticeably greater computational needs (0.527 seconds). The adaptive heuristic scoring mechanism and the combination of gradient descent and machine learning predictions, which successfully handled the combinatorial difficulties of COP, are responsible for GBPD's exceptional performance. These results offer useful information to manufacturers looking for scalable, effective optimization solutions. They also point to potential avenues for future research, such as extending the applicability of GBPD to more intricate production scenarios and further honing machine learning models for increased efficiency and adaptability.]]>
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