Abstract
This paper studies how to determine task allocation schemes according to the status and requirements of various teams, to achieve optimal performance for a knowledge-intensive team (KIT), which is different from traditional task assignment. The way to allocate tasks to a team affects task processing and, in turn, influences the team itself after the task is processed. Considering the knowledge requirement of tasks as a driving force and that knowledge exchange is pivotal, we build a KIT system model based on complex adaptive system theory and agent modeling technology, design task allocation strategies (TASs) and a team performance measurement scale utilizing computational experiment, and analyze how different TASs impact the different performance indicators of KITs. The experimental results show the recommend TAS varies under different conditions, such as the knowledge levels of members, team structures, and tasks to be assigned, particularly when the requirements to the team are different. In conclusion, we put forward a new way of thinking and methodology for real task allocation problems and provide support for allocation decision makers.
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