Modeling and Real-Time Scheduling of Large-Scale Batteries for Maximizing Performance

2015 ◽  
Author(s):  
Eugene Kim ◽  
Jinkyu Lee ◽  
Kang G. Shin
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Real Time Scheduling ◽  
Time Scheduling

scholarly journals An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing

2020 ◽  
Vol 10 (7) ◽  
pp. 2491
Author(s):  
Shengkai Chen ◽  
Shuliang Fang ◽  
Renzhong Tang
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Large Scale ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Matching Task ◽  
Task Completion ◽  
Ann Model ◽  
Real Time Scheduling ◽  
Time Scheduling

The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type matching, task precedence, resource occupation, and logistics duration, need to be met, and the internal manufacturing plan of providers must also be considered. Since the platform aggregates massive manufacturing resources to serve large-scale manufacturing tasks, the space of feasible solutions is huge, resulting in many conventional search algorithms no longer being applicable. In this paper, we considered resource allocation as the key procedure for real-time scheduling, and an ANN (Artificial Neural Network) based model is established to predict the task completion status for resource allocation among candidates. The trained ANN model has high prediction accuracy, and the ANN-based scheduling approach performs better than the preferred method in terms of the optimization objectives, including total cost, service satisfaction, and make-span. In addition, the proposed approach has potential in the application for smart manufacturing or Industry 4.0 because of its high response performance and good scalability.

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