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This paper develops a method system for efficient task assignment in product R&amp;D. The method system consists of three components: similar tasks identification, tasks\u2019 execution time calculation, and task assignment model. The similar tasks identification component entails the retrieval of a similar task model to identify similar tasks. From the knowledge-based view, the tasks\u2019 execution time calculation component uses the BP neural network to predict tasks\u2019 execution time according to the previous similar tasks and the Task\u2013Knowledge\u2013Person (TKP) network. When constructing the BP neural network, the satisfaction degree of knowledge and the execution time are set as the input and output, respectively. Considering the uncertain factors associated with the whole R&amp;D process, the task assignment model component serves as a robust optimization model to assign tasks. Then, an improved genetic algorithm is developed to solve the task assignment model. Finally, the results of numerical experiment are reported to validate the effectiveness of the proposed methods.<\/jats:p>","DOI":"10.1155\/2020\/3543782","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T23:30:44Z","timestamp":1599175844000},"page":"1-12","source":"Crossref","is-referenced-by-count":7,"title":["A Method for Efficient Task Assignment Based on the Satisfaction Degree of Knowledge"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6001-5744","authenticated-orcid":true,"given":"Jiafu","family":"Su","sequence":"first","affiliation":[{"name":"Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing, China"},{"name":"National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 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