Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity

Abstract

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.

Impacts of Burnishing Variables on the Quality Indicators in a Single Diamond Burnishing Operation

“…Step 4: The optimal data of the burnishing parameters and responses are selected using the nondominated sorting genetic algorithm based on the grid partitioning (NSGA-G) [22]. The operation steps of the NSGA-G are expressed in Fig.…”