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This paper aims to investigate the optimal sorting of orders reflecting on the material changing lead time over the machines in the roofing manufacturing industry. Specifically, a number of jobs were sorted together based on the material used and then consolidated for subsequent processes, i.e., assigned to the corresponding machines. To achieve the optimal sorting for the received orders, a combinatorial dispatch rule was proposed, which were Earliest Due Date (EDD), First In First Out (FIFO), and Shortest Processing Time (SPT). The sequence of orders organized by the scheduling algorithm was able to minimize the changing material lead time and also maximize the number of orders to be scheduled in the production. Consequently, on-time delivery could be achieved. Tests based on real data have been set up to evaluate the performance of the proposed algorithm in sorting the received orders. As a result, the proposed algorithm has successfully reduced the material changing lead time by 47.3% and 40% in the first and second tests, respectively.

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The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

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University timetabling construction is a complicated task that is encountered by universities in the world. In this study, a hybrid approach has been developed to produce timetable solution for the university examination timetabling problem. Black Hole Algorithm (BHA), a population-based approach that mimics the black hole phenomenon has been introduced in the literature recently and successfully applied in addressing various optimization problems. Although its effectiveness has been proven, there still exists inefficiency regarding the exploitation ability where BHA is poor in fine tuning search region in reaching for good quality of solution. Hence, a hybrid framework for university examination timetabling problem that is based on BHA and Hill Climbing local search is proposed (hybrid BHA). The aim of this hybridization is to improve the exploitation ability of BHA in fine tuning the promising search regions and convergence speed of the search process. A real-world university examination benchmark dataset has been used to evaluate the performance of hybrid BHA. The computational results demonstrate that hybrid BHA capable of generating competitive results and recording best results for three instances, compared to the reference approaches and current best-known recorded in the literature. Other than that, findings from the Friedman tests show that the hybrid BHA ranked second and third in comparison with hybrid and meta-heuristic approaches (total of 27 approaches) reported in the literature, respectively.

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