A new SMAA-based methodology for incomplete pairwise comparison matrices: evaluating production errors in the automotive sector
针对汽车生产中专家提供的成对比较矩阵不完整的问题，提出基于随机多目标可接受性分析的新方法，通过变化缺失条目给出各选项达到特定排名的概率，帮助全面评估生产错误。
Analysing and mitigating errors in production processes is a primary objective of companies in the automotive sector. Unfortunately, due to inaccurate or partially missing information, comparing errors is often very difficult, resulting from the experts’ provision of incomplete pairwise comparison matrices. In the literature, several techniques have been developed to complete such matrices. These techniques merely estimate what the decision makers or experts would have entered according to known entries. In this article, we propose a new methodology based on the stochastic multi-objective acceptability analysis; we apply it to vary the missing entries of the pairwise comparison matrix, thus providing the probability that an alternative/criterion will attain a given rank. This approach gives a complete view of the possible outcomes because it represents all possible decision maker mindsets. We present a case study carried out in a multinational automotive industry where we apply our methodology for evaluating errors in the production process.