Forecasting the Equipment Effectiveness in Total Productive Maintenance Using an Intelligent Hybrid Conceptual Model

Production managers are forced to achieve higher levels of operating performance due to the complexity of today\u27s production environment. The accuracy of manufacturing facilities usually has an impact on productivity. Thus, forecasting machine performance has become an inevitable responsibility of production managers. However, the question of how managers may effectively evaluate the effectiveness of equipment remains unresolved. Although this topic has not been given much consideration in earlier studies, the production environment of today makes it significant. In order to predict the equipment effectiveness, this study proposes two different prediction models. The models are Adaptive Neuro Fuzzy Inference System (ANFIS) and hybrid firefly algorithm-adaptive neuro fuzzy inference system (FA-ANFIS). The equipment effectiveness prediction model has been developed and evaluated using a real-world case from a textile processing industry. As a result, the proposed hybrid FA-ANFIS model outperforms with a high accuracy of 99.1 percent and a low root-mean-square error (RMSE) of 0.090766. Moreover, this proposed model helps production managers in evaluating the equipment effectiveness

Časovno-stroškovna optimizacija produktivnosti dela z modelom na osnovi ekstremalnega – mikrogenetskega algoritma

In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively