Predominance Preferential Selection for Minimizing Surplus Parts in the Selective Assembly of a Flow Production System

This paper presents the Predominance Preferential Selection (PPS) algorithm, an advanced approach to optimizing selective assembly in flow production systems. PPS enhances matching efficiency and reduces surplus parts by prioritizing component selection based on dimensional similarity. Unlike the Density-Based Prioritization (DBP) approach, PPS improves process capability and computational efficiency while maintaining dimensional diversity within slot structures. By grouping components with similar dimensions, PPS minimizes search space, achieving a matching rate increase from 95.11% to 97.09% and reducing computational time by 83%. Validation with real-world process data from a precision bearing manufacturer confirmed a 40% reduction in surplus parts, demonstrating its effectiveness. Although a slight decrease in process capability index (Cpk) was observed, the benefits of improved matching rates and efficiency outweigh this drawback. PPS is highly scalable and adaptable to multi-slot row structures, making it suitable for various flow production systems. This study contributes to advancing selective assembly methodologies, offering a computationally efficient and adaptable solution for precision manufacturing.