Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
Source abstract
Abstract
In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it only references past records. This paper proposes a new self-organizing hierarchical particle swarm algorithm (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC)-support vector machine (SVM) regression model to increase the accuracy of lead-time prediction by combining the advanced PSO and SVM models. Moreover, this paper compares the prediction results of each SVM-based model with those of other conventional machine-learning algorithms. The results demonstrate that the proposed NHPSO-JTVAC-SVM model can achieve further meaningful enhancements in terms of prediction accuracy. The prediction performance of the NHPSO-JTVAC-SVM model is also better than that of the other SVM-based models or other machine learning algorithms. Overall, the NHPSO–JTVAC-SVM model is feasible for predicting the lead time in shipbuilding.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 08826, Korea
Research Institute of Marine Systems Engineering, Seoul National University, Seoul 08826, Korea
Funding
Ministry of Trade, Industry and Energy
References (42)
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