Application of Improved Particle Swarm Optimization for Sustainable Scheduling

As global carbon standards become more stringent, such as the European Union’s (EU) Carbon Border Adjustment Mechanism (CBAM), the manufacturing industry is facing a greater carbon tax burden and more intense competition. However, existing studies assume often that equipment operates under ideal and stable conditions, overlooking the impact of equipment deterioration on carbon emissions. In practice, machinery replacement in manufacturing is rarely conducted in a one-time, comprehensive manner; instead, regular maintenance and repairs are performed until the equipment approaches its end-of-life threshold. During this deterioration phase, aging equipment, increasing failure rates, and declining energy efficiency contribute to additional carbon emissions, posing challenges for effective carbon emissions management. Therefore, achieving sustainable scheduling that balances production efficiency and carbon emission control during the deterioration phase is crucial. This study focuses on the deterioration -induced carbon emissions in the molding process of the semiconductor assembly and test industry. A hybrid model integrating production scheduling and carbon emission optimization is developed, and an improved particle swarm optimization (IPSO) algorithm is employed for solution optimization. The IPSO dynamically adjusts inertia weights and fitness evaluation mechanisms to adaptively eliminate historically observed local and global worst solutions, thereby enhancing search efficiency and solution quality. This study provides enterprises with an intelligent scheduling approach that optimizes production efficiency while mitigating additional carbon emissions during the deterioration period, enabling green manufacturing and enhancing market competitiveness in the face of equipment status uncertainties.