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

The aim of this study is to examine the effect of motivation, attitude, and self-efficacy beliefs of secondary school students towards science course on their academic achievement. The relational survey model was used to conduct the study. A total of 635 students, 319 females, and 316 males participated in the study. “Motivation Scale for Learning Science”, “Self-Efficacy Scale for Science”, “Attitude Scale for Science Course” were used as data collection tools. In the study, independent t-test was used to make comparisons in terms of gender, and path analysis using structural equation modeling principles was conducted to determine the direction and level of the relationship between the variables. In the results of the research, significant differences were found in students’ attitudes, motivation, and self-efficacy in terms of the gender variable. It was concluded that students’ attitudes and motivation directly affect their academic achievement. There was a strong positive relationship between attitude and motivation, a low positive relationship between self-efficacy and attitude, and a moderate positive relationship between self-efficacy and motivation. In line with the results of this research, and considering the direct effects of academic achievement on students’ motivation in science course, it is recommended that necessary information and training activities be provided to teachers in order to carry out studies on increasing students’ motivation.