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.
