Cloud model improved TOPSIS for comprehensive evaluation of system evolvability

To address the core bottlenecks in the evolutionary performance evaluation of automobile door stamping processes, namely insufficient adaptation to dynamic uncertain information and the lack of systematic methodological support in existing methods, this study establishes a complete evaluation methodological framework covering the indicator system, weight algorithm, and evaluation model. First, based on the PDCA cycle logic, a multi-stage indicator system integrating static benchmarks and dynamic evolution dimensions is designed, covering the entire process of planning, execution, inspection, and optimization. Second, an entropy weight-information cloud coupled weighting algorithm is proposed. The entropy weight method is used to extract objective data features, and the fuzzy-random modeling capability of the information cloud model is combined to achieve robust weight allocation of evolutionary indicators. Finally, to solve the problem that the traditional TOPSIS method cannot distinguish the advantages and disadvantages of schemes near the ideal solution, JS divergence is introduced to improve the traditional TOPSIS algorithm, and the distance between the process scheme and the positive/negative ideal solutions is quantified to realize accurate performance ranking. Through case validation on automobile door stamping processes, five typical door stamping process schemes are evaluated. The results show that the comprehensive performance score ranges from 0.1765 to 0.8689, and the performance ranking is highly consistent with the actual production logic, verifying the effectiveness and industrial adaptability of the proposed methodology. This study provides a systematic quantitative tool for the evolutionary performance evaluation of processes in complex manufacturing scenarios, and has important theoretical reference and engineering application value.