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Product family design (PFD) is a widely adopted strategy for product realization, especially when design requirements are diversified and multi-faceted. Due to ever-changing customer needs and the increasingly complex and integrated product design structure, PFD and its optimization have been concerned more about a rapid and contextual product analysis and variant derivation based on a multi-objective optimization scheme subject to design concerns, which are often cross disciplinary, such as product service, carbon footprint, user experience, esthetics, etc. Existing PFD modeling approaches, which are primarily structured using component attributes and assembly relationships, possess notable limitations in representing complex component and design relationships. Hence, it has restricted comprehensive PFD analysis in an agile and contextual manner. Previously, we have studied and demonstrated the feasibility of using ontology for product family modeling and have suggested a framework of faceted information search and retrieval for product family design. In this paper, several new perspectives towards PFD based on ontology modeling are presented. Firstly, new metrics of ontology-based commonality that better reveal conceptual similarity under various design perspectives are formed. Secondly, faceted concept ranking is proposed as a new ranking approach for ontology-based component search under complex and heterogeneous design requirements. Thirdly, using these ranked results, a platform selection approach that considers a maximum aggregated ranking with a minimal platform modification among various platform choices is researched. From the selected platform and the newly proposed metrics, a modified multi-objective evolutionary algorithm with an embedded feature of configuration incompatibility check is studied and deployed for the optimal selection of components. A case study of PFD using four laptop computer families is reported as our first attempt to showcase how faceted component analysis, selection, and optimization can be accomplished based on the proposed family ontology.

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The best teacher preparation programs emphasise subject matter mastery and provide many opportunities for preservice teachers to spend time in real classrooms under the supervision of an experienced mentor. Thus, teaching practice plays utmost important component of teacher training in order to produce a high quality of future teacher due to the lacking of opportunity or comfort level to work on their pedagogy knowledge while managing their first class. But this condition is slightly different for in-service teacher with diploma and upgrade to degree through the in-service teacher degree programme (PPG) path. PPG is conducted in 4 years using distance learning mode. In-service teachers will attend the courses during weekend and only five times face-to-face meeting with the lecturers. PPG aims to improve the quality of teaching and school management by increasing the teachers' level of educational qualifications. However, issues were highlighted where teaching competency or performance among in-service teachers are varied during teaching practice observation. Due to their part-time mode of learning, there are concerns whether their performance is at par with their full time peers. Therefore, this paper aims to discuss the assessment of teaching practice competency among the PPG students. This study was applied the Documentation analyses as the research design by using the Teaching Assessment Form of Faculty of Technical & Vocational Education, Universiti Tun Hussein Onn Malaysia. Descriptive statistic using mean and standard deviation were used to analyses the collected data. The result showed that the competency of in-service teachers in teaching practicum in descending order is Personality>Portfolio> Implementation of Teaching> Lesson Planning> Implementation off Innovation> Justification of Innovation>Reflection. Results also showed that inservice teachers are good in affective (M=4.33, SD=0.48), follow by psychomotor (M=4.12, SD=0.47) and cognitive (M=3.99, SD=0.56). In conclusion, in-service teachers need training for innovation in teaching and emphasise in cognitive taxonomy.