Uncertain learning curve and its application in scheduling

The learning curve is a powerful tool for estimating either the production cost or time in production analysis. Our work focuses on the characterization of the uncertain nature of the learning effect. Traditionally, the learning curve of a new product can be derived by processing the past data obtained for similar products. However, due to the complicated environment, in some cases, the data for similar products may be lacking, preventing the use of the traditional methods to find the learning curve. To address this issue, we develop an uncertain learning curve by utilizing uncertainty theory. Moreover, some useful theorems are developed to characterize the uncertain learning curve. An experiment is conducted to compare the proposed model and the standard model. Finally, we apply the uncertain learning curve to the single-machine optimization problem.