A Novel Grey Incidence Decision-making Method Based on Close Degree and Its Application in Manufacturing Industry Upgrading

Targeting the problem of scheme ranking and indicator weighting that exist in grey incidence decision-making, a novel grey incidence decision-making method based on close degree is proposed, which can effectively distinguish evaluation results to the greatest extent. In this paper, we firstly define the concepts of the original and normative observation matrices, the vector normalization operator, and the data sequences of the positive and negative ideal systems’ behavioral characteristics. Then, the synthetic grey incidence coefficient is represented by the areas enclosed by two adjacent points between the scheme and the ideal sequences. This area is utilized to measure the proximity of two sequences in distance and their geometric similarity. On the basis of traditional weighting methods, the subjective-objective combined weighting method which is based on level difference maximization is employed to assign weights to indicators. We also provide theoretical proof that the weighting method is more reasonable and interpretable than traditional methods. Subsequently, we propose the close degree of grey incidence by employing the synthetic grey incidence coefficient and the subjective-objective combined weighting method, so that we can implement the scheme ranking. Finally, we take the evaluation of the status of manufacturing industry upgrading in the Yangtze River Delta (YRD) as a case analysis, and explore the theoretical and practical value of the proposed method.