Mastering Evaluation Model Solving: Preprocess, Weight, and Integrate with Python

1 Solving the Evaluation Model

Overall, solving the HiMCM 2020A evaluation model requires little programming, but proficiency saves time. Input data can be viewed as a table where each row represents a job and each column an attribute, i.e., a matrix. The task is to perform row‑column transformations, essentially matrix operations.

The evaluation model computation includes three main stages: data preprocessing (standardization and normalization), weight calculation, and the integration of weights with data.

1.1 Preprocessing

Consistency processing converts small, medium, and interval indicators into large indicators, using piecewise functions, reciprocals, or negatives. In Excel this is done with formulas; in Python it can be handled with either NumPy or Pandas and matrix operations.

Normalization includes range normalization, vector normalization, standardization, and proportion normalization. Functions such as linalg.norm , np.mean , and np.std from NumPy are commonly used, though custom functions can also be written.

1.2 Weight Calculation

Weight calculation is relatively complex and often the most challenging part of the evaluation model. Using the entropy weight method as an example, an entropy function is required, and care must be taken to avoid division by zero (e.g., replace zero with a very small number such as 0.000001).

The Analytic Hierarchy Process (AHP) requires eigenvalues and eigenvectors; these can be obtained with NumPy’s linalg.eig function. Understanding these concepts is essential for clear presentation in a paper.

1.3 Integrating Weights and Data

Integration methods include linear weighting, exponential weighting, and logarithmic weighting. Linear weighting is most common; it relies on matrix multiplication, which can be performed with NumPy’s dot function or the @ operator.

2 Summary

For most students familiar with mathematical modeling, the evaluation model computation is not a difficulty; the focus should be on innovative model design and interpretation. Programming-wise, basic knowledge of Python’s NumPy or Pandas suffices, though other models may require additional specialized techniques.