Abstract

We present an optimized "cross-assignment" program for large-scale innovation contests. The program aims to improve work allocation and fair judging by utilizing the optimal objective function method. Our study found that the two-stage and weighted evaluation schemes are more effective than traditional judging schemes. However, there are still some shortcomings in the current system. To address these issues, we propose an improved two-stage evaluation scheme that normalizes scores with a normal distribution and uses the Borda sorting technique to categorize submissions into five groups for judges to evaluate based on their perceptions. We also detail a method for weighting tied scores to determine final rankings. Testing showed that this approach yields a Normalized Discounted Cumulative Gain (NDCG) of 0.8667, indicating greater fairness and precision in the assessment of submissions.