Cloud computing (CM) is bringing opportunities to various fields. In the manufacturing sector, cloud manufacturing (CMfg) borrows many useful concepts from CM. Among them, simulating a factory online is imperative. To this end, the time required for a simulation task needs to be estimated first. However, this topic has rarely been discussed. An artificial neural network (ANN) approach is proposed in this paper. In the proposed methodology, an ANN is constructed to estimate the required execution time for a simulation task. The real data of 90 simulation tasks have been collected to validate the proposed methodology. In addition, several existing methods were also applied to these tasks for a comparison. According to the experimental results, the proposed methodology outperformed the compared existing methods in improving the estimation accuracy. In addition, the planning horizon is the most decisive factor to estimating the execution time of a simulation task.
|Number of pages||12|
|Journal||International Journal of Internet Manufacturing and Services|
|State||Published - 1 Jan 2014|
- Artificial neural network
- Cloud manufacturing
- Workload estimation