Application of Machine Learning Methods for Pallet Loading Problem

Because of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery survive against their competitors. For this reason, logistics play a significant role warehousing, shipments, and transportation products. Therefore, high utilization resources can improve profit margins reduce unnecessary storage or shipping costs. One issue shipments is Pallet Loading Problem (PLP) which generally be solved by seeking maximize total number boxes loaded on pallet. In many previous studies, various solutions PLP have been suggested context shipment systems. paper, novel two-phase approach presented utilizing Machine Learning (ML) models tackle PLP. The dataset utilized study was obtained from DHL supply chain system. According training testing ML models, our results show that very (>85%) Utilization Volume (PUV) obtained, an accuracy >89% determined predict accurate loading arrangement suitable Furthermore, comprehensive analysis all basis comparison several provided order efficacy proposed methodology.