Due Date Assignment in a Dynamic Job Shop with the Orthogonal Kernel Least Squares Algorithm

4th International Conference on Manufacturing and Industrial Technologies

机械制造;工业技术

Yang, D.H.^1 ; Hu, L.^1 ; Qian, Y.^1

School of Business Administration, Shenzhen Institute of Information Technology, Shenzhen, China^1

关键词: Back-propagation neural networks;    Due-date assignment;    Job characteristics;    Job shop;    Least squares algorithm;    Manufacturing industries;    Prediction performance;    Shortest Processing Time;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/212/1/012022/pdf
DOI  :  10.1088/1757-899X/212/1/012022

学科分类：工业工程学

来源: IOP

【 摘 要 】

Meeting due dates is a key goal in the manufacturing industries. This paper proposes a method for due date assignment (DDA) by using the Orthogonal Kernel Least Squares Algorithm (OKLSA). A simulation model is built to imitate the production process of a highly dynamic job shop. Several factors describing job characteristics and system state are extracted as attributes to predict job flow-times. A number of experiments under conditions of varying dispatching rules and 90% shop utilization level have been carried out to evaluate the effectiveness of OKLSA applied for DDA. The prediction performance of OKLSA is compared with those of five conventional DDA models and back-propagation neural network (BPNN). The experimental results indicate that OKLSA is statistically superior to other DDA models in terms of mean absolute lateness and root mean squares lateness in most cases. The only exception occurs when the shortest processing time rule is used for dispatching jobs, the difference between OKLSA and BPNN is not statistically significant.