Abstract
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.
Highlights
The development of the Internet, automation, intelligent decision support, and other technologies has driven the manufacturing industry to transform towards digitalization, networking, and intelligence [1,2,3]
The set-up time for MT is already included in the processing time; No interruption is considered in the processing of MTs; The capacity of Manufacturing Resource (MR) occupied by task processing will be released when the processing is completed; Transportation logistics need to be considered before and after the processing of MT
The average decision time for MR allocation is under 50 ms, and the average decision time for determining a schedule is under 40s, which indicates that the ANN-based approach is suitable for the real-time scheduling because, compared to the NSGA-II, the proposed ANN-based approach only takes 4.4% of the decision time to determine a sound schedule in such a discrete manufacturing environment, and the decision time is negligible if compared to the duration of resource configuration, manufacturing execution, transportation, and so on, which are usually measured in hours or days
Summary
The development of the Internet, automation, intelligent decision support, and other technologies has driven the manufacturing industry to transform towards digitalization, networking, and intelligence [1,2,3]. The scheduling problem in the CMfg environment is as follows: how to reasonably allocate the emerging MTs to the type-matched MRs in real-time under the constraints of task precedence, resource occupation, and logistics duration while reducing the service costs, improving the service quality, and shortening the make-span for each MP?. The keys to designing such an approach are the neighborhood structure and the search direction, such as Simulated Annealing, Tabu-search, Discrete Search, Genetic Algorithm, and so on [19,20,21,22,23] These algorithms cannot be directly applied to the scheduling problem in the CMfg environment because they will take a long time for the solution searching. Research on the real-time scheduling in the CMfg is scarce, and the proposed scheduling methods are based on reactive scheduling strategy, the baseline of which is set in advance and needs to be modified as the environment changes.
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