Digital twin (DT) is a core technology that enables the integration among physical machines, tools, material handling and warehousing, and real-time manufacturing decisions. In this study, a DT framework is proposed for the real-time monitoring of conventional machines to connect isolated machines to an interconnected system and monitor machine conditions in real-time. We implement a DT for a die cutting machine. A dashboard-based mission centre is created to display the real-time condition of the machine. The mission centre is composed of three main functions: real-time machine monitoring, overall equipment effectiveness, and order scheduling. Compared with existing approaches with a barrier of high investment and/or high design complexity, this study proposes an economic DT framework composed of conventional machine structure but has a tolerable level of industrial Internet of things capability.

In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.

The positioning and layout of facilities on a construction site is important to enhance efficiency, productivity and safety. In this paper, three metaheuristics, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), are proposed to solve the construction site layout problem in which facilities are positioned to locations so the sum of construction cost and interactive cost due to facility layout constraints is minimized. The craziness concept, cross-mutate and scramble mutation techniques are used to increase the diversity of the solutions and to keep the algorithms from being trapped at local optima. The optimal parameters for each algorithm are determined by using the Design of Experiment approach (DOE). Two case studies of facility layout problem derived from literature were used to rigorously compare the performances of the three algorithms, in terms of effectiveness, efficiency, and consistency. ANOVA test was used to compare the performances. The results demonstrate the capability of the modified method in solving facility layout problems effectively, efficiently and consistently. This study contributes to the decision making when determining an appropriate solution for the construction site layout problem.

In 2013, the German government implemented the strategic initiative Industry 4.0 to establish itself as a leader in advanced manufacturing and the transformation to digitalisation. Numerous manufacturers continue to explore ways to evaluate and enhance their capabilities to tackle market competition. This empirical study adopts a maturity model that is based on the Economic Development Board’s Singapore Smart Industry Readiness Index. The model will allow companies to conduct self-assessments so that they can systematically and comprehensively align themselves with Industry 4.0. This study conducts a clustering analysis to explore the maturity of Industry 4.0 in Taiwan, the clustering’s index factors, and the impact of key factors on companies’ self-assessment. Classifying 80 Taiwan enterprises into four categories, it shows a significantly positive correlation among process, technology, and organisation. Further, a majority of the 80 companies tested under the maturity model still appear to be immature or partially mature, and needing much improvement and the re-evaluation of their transformation strategies related to Industry 4.0. Finally, the Singapore Smart Industry Readiness Index is suitable to conduct self-assessments in Taiwan-based enterprises. These findings could serve as useful maturity and grouping guidelines for practitioners and researchers.