Use of neural networks to achieve dynamic task allocation: a flexible manufacturing system example

Abstract To attain optimum performance of the automated system, task allocation between human and computer becomes very important. However, a critical problem existing in the technology of dynamic task allocation is how to develop an implicit human–computer communication interface. Two models of `neural network' and `predictive method' are proposed in this study to allocate the task between the human and the computer. The first phase in this study was to find some important and sensitive indexes to measure the mental workload in supervisory task through the multiple regression equation. The second phase of this study was to construct a programming system in an FMS to evaluate the workload index and allocate the task dynamically through the application of the back propagation network (BPN) and the predictive values of the multiple regression equation. Twenty-two subjects attended the experiment and were divided into two groups, one was the dynamic group and the other was the static group. The result showed that the workload of the dynamic group was significantly lower than the static group ( p -value=0.0426 α =0.05). The neural network proved to be an effective method for decreasing the mental workload through dynamic task allocation. Relevance to industry The mental workload of the operator must be considered when designing an FMS. The results of this study show that the method of neural network can be applied to the design of dynamic task allocation in the control room of FMS.

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