In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ...In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.展开更多
This paper studies how to determine task allocation schemes according to the status and require-ments of various teams, to achieve optimal performance for a knowledge-intensive team （KIT）, whichis different from tra...This paper studies how to determine task allocation schemes according to the status and require-ments of various teams, to achieve optimal performance for a knowledge-intensive team （KIT）, whichis different from traditional task assignment. The way to allocate tasks to a team affects task processingand, in turn, influences the team itself after the task is processed. Considering the knowledge require-ment of tasks as a driving force and that knowledge exchange is pivotal, we build a KIT system modelbased on complex adaptive system theory and agent modeling technology, design task allocation strat-egies （TASs） and a team performance measurement scale utilizing computational experiment, and an-alyze how different TASs impact the different performance indicators of KITs. The experimental re-sults show the recommend TAS varies under different conditions, such as the knowledge levels ofmembers, team structures, and tasks to be assigned, particularly when the requirements to the team aredifferent. In conclusion, we put forward a new way of thinking and methodology for real task alloca-tion problems and provide support for allocation decision makers.展开更多
文摘In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.
文摘This paper studies how to determine task allocation schemes according to the status and require-ments of various teams, to achieve optimal performance for a knowledge-intensive team （KIT）, whichis different from traditional task assignment. The way to allocate tasks to a team affects task processingand, in turn, influences the team itself after the task is processed. Considering the knowledge require-ment of tasks as a driving force and that knowledge exchange is pivotal, we build a KIT system modelbased on complex adaptive system theory and agent modeling technology, design task allocation strat-egies （TASs） and a team performance measurement scale utilizing computational experiment, and an-alyze how different TASs impact the different performance indicators of KITs. The experimental re-sults show the recommend TAS varies under different conditions, such as the knowledge levels ofmembers, team structures, and tasks to be assigned, particularly when the requirements to the team aredifferent. In conclusion, we put forward a new way of thinking and methodology for real task alloca-tion problems and provide support for allocation decision makers.
