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

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.