Assembly makespan estimation using features extracted by a topic model

Abstract

Accurate makespan estimation is imperative during production scheduling to increase the flexibility and efficiency of work plans. However, given the complexities of production systems and product customizations, it is challenging to estimate makespans with high accuracy. In this paper, we propose a topic model-based neural network (TM-NN) method to increase the accuracy of makespan estimation for assembly processes. First, unlike traditional methods that use influential factors as inputs, we extract assembly features using a latent Dirichlet allocation model that mines latent topic information from an assembly instruction corpus. Then, the assembly process is represented as a sequence model with both assembly topics and features of the product physical characteristics, the assembly process, the equipment, the personnel, and uncertainty. Finally, we use a structured numerical vector as the input to machine learning-based predictive models, including a neural network, a random forest, and a support vector machine, and estimate makespans. The results show that the proposed TM-NN method effectively extracts latent topics in assembly documents and significantly increases the accuracy of makespan estimation.