Abstract
For the product R&D process, it is a challenge to effectively and reasonably assign tasks and estimate their execution time. This paper develops a method system for efficient task assignment in product R&D. The method system consists of three components: similar tasks identification, tasks’ 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’ execution time calculation component uses the BP neural network to predict tasks’ execution time according to the previous similar tasks and the Task–Knowledge–Person (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&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.
Highlights
Nowadays, manufacturing industries are facing challenges arising from continuous innovations, complex competition, and collaboration environment [1, 2]
This paper proposed a method system for efficient task assignment considering the satisfaction degree between task and knowledge. e method system includes three components, which are the similar tasks identification, tasks’ execution time calculation, and task assignment model
In order to calculate tasks’ execution time by certain team more accurately, a method is proposed to incorporate the similarity between the knowledge structure of the Research and Development (R&D) team and the knowledge needed by the task using the BP neural network
Summary
Nowadays, manufacturing industries are facing challenges arising from continuous innovations, complex competition, and collaboration environment [1, 2]. This paper proposed a method system for efficient task assignment considering the satisfaction degree between task and knowledge. En, a robust task assignment model considering uncertainty and a genetic algorithm are proposed to obtain the optimal task assignment scheme for the product R&D. e following sections will discuss the three components of the ETA system in detail: 3.1. In order to calculate tasks’ execution time by certain team more accurately, a method is proposed to incorporate the similarity between the knowledge structure of the R&D team and the knowledge needed by the task using the BP neural network. For the task assignment between task Ti and team Pk, the input of the BP neural network is knowledge satisfaction degree Skαi, and the output is task’s execution time tik. We choose the function of Sim to predict tasks’ execution time
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