Abstract
In the rapidly evolving industrial landscape, Artificial Intelligence (AI) has emerged as a transformative force in various sectors, including manufacturing and supply chain management. Meanwhile, packaging planning is another area that is still open to AI development. Effective packaging planning is a complicated task to be handled carefully throughout the entire planning process. To solve this problem, we propose an improved heterogeneous chain-based multi-output classification model for predicting the dimensions and types of packages for each shipment. While conventional chain regression models typically employ only a single base classifier within each chain, our improved model allows for flexibility in the utilization of different classifiers within a chain structure. Our improved model is analyzed on a real-world dataset by employing different multi-output classification algorithms, including Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN). Experimental results demonstrate that our model, based solely on DT, outperforms by obtaining the best accuracy with a 0.98 overall accuracy value compared to traditional multi-output classification and chain regression models. Also, the proposed model with different classifiers has the second-best overall accuracy result in all models and has a higher overall accuracy result than traditional chain-based models.