The dual-phase learning–forgetting model

Abstract

In this paper we develop the dual-phase learning–forgetting model (DPLFM) to predict task times in an industrial setting. The DPLFM results from integrating previously published learning and forgetting models that have been validated using experimental data. As a result, the DPLFM captures two important characteristics of the individual learning and forgetting phenomenon that are found in industrial settings. First, it expresses learning as a combination of cognitive and motor skills learning. This allows the learning rate to vary based on the experience gained in previous cycles, as well as the nature of task being performed (e.g. highly complex versus very simple tasks). Second, our model also captures forgetting based on the worker's learning rate, prior experience, as well as the length of the interruption interval over which the worker experiences forgetting. We use a numerical example to illustrate the DPLFM, and perform an analysis on its parameters to gain insights into its behavior.