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Landslides are a geological phenomenon that is causing considerable economic and human losses annually in various regions of the world. In some cases, the complex behaviors of some such phenomena cause that single machine learning models fail in modeling them well. To overcome this issue, this paper presents two novel genetic-algorithm (GA)-based ensemble models constructed with the decision tree (DT), k-nearest neighbors (KNN), and Naive Bayes (NB) models based on the bagging and random sub-space (RS) methods for landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan. Sixteen factors, including topographic, climatic, human, and geological factors were used as possible factors that influence landslide occurrence in the study area. In addition to this, one hundred and ninety two landslide locations were employed for training and testing the models. The GA was used in this study for feature selection based on three models: DT, KNN, and NB. Model performance evaluation based on the area under the receiver operating characteristic (AUROC) curve indicated that the ensemble models outperform the standalone ones. The values of the AUROC curves in the validation phases for the five models, namely, the GA-based DT, KNN, NB, bagging-based, and RS-based ensemble model, were 0.63, 0.69, 0.63, 0.89, and 0.95, respectively. The results of this study suggest that simple models can be combined using the bagging and RS methods to produce integrated models that have higher accuracy than that of any of the individual simple models. [ABSTRACT FROM AUTHOR]

To be in the competitive industrial world, industries required high quality, speed in completing the required work, and commitment to the delivery dates. One of the most important issues in the field of production management is the job shop scheduling problem (JSSP). In this paper, the researchers tried to solve JSSP of factory by presenting a method to improve the factory's production. Job shop scheduling (JSS) is a suitable method for solving these types of problems, which aims to improve the production flow through minimizing the whole operation time of the products. Moreover, considering the factory that depends on workers as same as machine, human factor should be considered while scheduling by using the workers' weightage, in order to improve the workers' working time flexibility in terms of their waiting time among their tasks by proposed model of JSS. In addition, the researchers proposed a new combination of weightage values by using Taguchi method, regarding to improve the workers' working time and using genetic algorithm (GA) to solve the proposed model of JSS. One of the factories which is located in Jordan, and it is considered as one of the important factories; nevertheless, it can cover the local demands hardly, and hence, it deserves to be as a study case for this research. The findings of the studies decreased the whole operation time of the products by saving 75 min for each production line and 90 min by using GA, and the proposed model improved the flexibility of the workers' working time in terms of their waiting times among their tasks. [ABSTRACT FROM AUTHOR]