A Hybrid Approach Combining Fuzzy c-Means-Based Genetic Algorithm and Machine Learning for Predicting Job Cycle Times for Semiconductor Manufacturing

Job cycle time is the of a job or required to complete job. Prediction critical task for semiconductor fabrication factory. A predictive model must forecast pursue sustainable development, meet customer requirements, and promote downstream operations. To effectively predict in factories, we propose an effective hybrid approach combining fuzzy c-means (FCM)-based genetic algorithm (GA) backpropagation network (BPN) time. All records are divided into two datasets: first dataset clustering training, other testing. An FCM-based GA classification method developed pre-classify several clusters. The results then fed BPN predictor. predictor can compare it with second dataset. Finally, present case study using actual obtained from factory demonstrate effectiveness efficiency proposed approach.