The Open Electrical & Electronic Engineering Journal

2014, 8 : 653-657
Published online 2014 December 31. DOI: 10.2174/1874129001408010653
Publisher ID: TOEEJ-8-653

The Dynamic Job Shop Scheduling Approach Based on Data-Driven Genetic Algorithm

Yanfang Yu and Yue Ying
Zhejiang Radio & Television University, Hangzhou 310030, P.R. China.

ABSTRACT

Rapid development of the Internet of Things not only provides large amounts of data to the job-shop scheduling, but also proposes a great challenge for dynamic job shop scheduling. A dynamic job shop scheduling approach is proposed based on the data-driven genetic algorithm. Application examples suggest that this approach is correct, feasible and available. This approach can provide the technical support for the long-term development of enterprises in the field of intelligent production.

Keywords:

Data-driven, dynamic optimization, genetic algorithm, job shop scheduling.