Elitist Genetic Algorithm and Elitist Ant Colony Optimization on Resource Scheduling in Field Cloud Manufacturing

Abstract

There have been several studies on the scheduling mechanism in cloud manufacture in on-factory manufacturing situations. However, scheduling mechanism in cloud manufacture in an off-factory situation (field cloud manufacturing) has not been widely studied. Even though there are many manufacturing tasks that need to be implemented using field manufacturing scheme. So in this study, a research on scheduling problems in field cloud manufacture system was carried out. The research process begins with creating a model for scheduling problem in field cloud manufacture. This model is designed by analyzing the workflow of field cloud manufacture system. Then by analyzing the assumptions and limitations contained in the field manufacturing scheme, the encoding and decoding methods of the scheduling model and the parameters used to measure the performance of the proposed solutions can be determined. After that, the Elitist Genetic Algorithm (EGA) and Elitist Ant Colony optimization (EACO) were applied to the scheduling problem model to carry out the process of finding optimal scheduling solutions. The results of this study showed that the Elitist Genetic Algorithm (EGA) and Elitist Ant Colony optimization (EACO) can be used to optimize the scheduling problem in field cloud manufacturing and the overall improvement of the optimized schedule scheme is improved by 40,3% by EGA and 3S,7S% by EACO. It can be seen that EGA and EACO suitable for optimizing the problems with large solution spaces like scheduling in field cloud manufacturing. But this study also shows that the performance of EGA is far superior both in terms of the value of the resulting fitness schedule and in terms of the time consumed to produce the schedule compared to EACO.