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Industries nowadays must be able to quickly adapt with the customer and improve product quality to survive in the competitive edge. Job shop scheduling is crucial in the manufacturing world and exists within most manufacturing sectors. In the manufacturing world, scheduling problems are extensively implementing the dispatching rules. The procedures are designed to provide good solutions to complex problems in real-time. This paper describes the importance of dispatching rules in improving the performance of the factory. This study evaluates total of44dispatching rules with the classification of hybrid and single rules. The performance of each rule compared and summarized to determine the final ranking for all the different dispatching rules. The result shown that MTWR (Most Total Work Remaining) rule performs well in almost all measurements as well as hybrid dispatching rules is not generating the best rules compared to single dispatching rule. A set of data from an automotive industry use to simulate the job-shop production floor.

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Unemployment is a current issue that happens globally and brings adverse impacts on worldwide. Thus, graduate employability is one of the significant elements to be highlighted in unemployment issue. There are several factors affecting graduate employability, traditionally, excellent academic performance (i.e., cumulative grade point average, CGPA) has been the most dominant element in determining an individual's employment status. However, researches have shown that not only CGPA determines the graduate employability; in fact other factors may influence the graduate achievement in getting a job. In this work data mining techniques are used to determine what are the factors that affecting the graduates. Therefore, the objective of this study is to identify factors that influence graduates employability. Seven years of data (from 2011 to 2017) are collected through the Malaysia's Ministry of Education tracer study. Total number of 43863 data instances involved in this employability class model development. Three classification algorithms, Decision Tree, Support Vector Machines and Artificial Neural Networks are used and being compared for the best models. The results show decision tree J48 produces higher accuracy compared to other techniques with classification accuracy of 66.0651% and it increased to 66.1824% after the parameter tuning. Besides, the algorithm is easily interpreted, and time to build the model is small which is 0.22 seconds. This paper identified seven factors affecting graduate employability, namely age, faculty, field of study, co-curriculum, marital status, industrial internship and English skill. Among these factors, attribute age, industrial internship and faculty contain the most information and affect the final class, i.e. employability status. Therefore, the results of this study will help higher education institutions in Malaysia to prepare their graduates with necessary skills before entering the job market.

Naïve Bayesian classifier is one of the most effective and efficient classification algorithms. The elegant simplicity and apparent accuracy of naive Bayes (NB) even when the independence assumption is violated, fosters the on-going interest in the model. This paper discusses issues on NB along with its advantages and disadvantages. We also present an overview of NB variants and provide a categorization of those methods based on four dimensions. These include manipulating the set of attributes, allowing interdependencies, employing local learning and adjusting the probabilities by numeric weights. Examples for each category are discussed based on 18 variants reviewed in this paper.