Bio-Inspired Computing-Based Multi-Objective Optimization For Sustainable Manufacturing In The Industry 4.0 Era

Abstract

This study aims to evaluate the contribution of bio-inspired computing towards the sustainability of manufacturing systems in the context of Industry 4.0. Using quantitative and design approaches, data were collected from 100 professional respondents in the manufacturing sector through questionnaires and structured interviews. Statistical analysis was performed using Pearson correlation, linear regression, t-test, and ANOVA with the help of SPSS software. The results showed a very strong and significant relationship between the use of bio-inspired algorithms, such as Particle Swarm Optimization (PSO), with energy efficiency (r = 0.872), production level (r = 0.723), and environmental sustainability (r = 0.790). Linear regression showed that the use of the technology explained 76.1% of the variability in energy efficiency (R² = 0.761; p < 0.001). The ANOVA results also showed significant differences between groups of technology users in terms of efficiency achievements. These findings indicate that bio-inspired computing can be an important strategy in digital transformation and more sustainable decision-making. This study contributes to developing multi-objective optimization theory and provides practical implications for industrial management in implementing adaptive and environmentally friendly technologies.