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ABSTRACT
In this paper, a relatively new and mathematically potent tool of MCDM (Multi-Criteria Decision Making) in the form of EDAS (Evaluation based on Distance from Average Solution) approach has been proposed for ranking of thirteen candidate cotton fabrics on the basis of four fabric parameters/attributes namely cover, thickness, areal density, and porosity. The ranking and selection of the candidate fabrics have been done with a view to achieving optimal thermal comfort properties. Sample no. 3 with highest appraisal score of 0.9838 achieves rank 1 (best choice) whereas sample no. 6 with lowest appraisal score of 0.0000 occupies rank 13 (worst choice). The ranking results obtained by the proposed method demonstrates a significant agreement in ranking performance with the earlier methods, which is evidenced by very high rank correlation coefficients (Rs >0.87). Ranking patterns given by four imaginary weight sets also possess very high degree of agreement with rank correlation coefficients higher than 0.90. Moreover, there is no occurrence of rank reversal even when the initial decision-making matrix is changed. Thus, sensitivity analyses based on changing the criteria weights and that through influence of dynamic decision matrices further bolster the stability and robustness of the proposed approach in terms of ranking performance.
本文提出了一种新的、数学上有效的多准则决策工具，即基于距离平均解的评价（EDAS）方法的多准则决策（MCDM）方法，根据织物的四个参数/属性，即覆盖率、厚度、面积密度、织物厚度、织物表面密度，对13种棉织物进行排序，以及孔隙度. 为了达到最佳的热舒适性能，对候选织物进行了排序和选择。评价得分最高为0.9838的3号样品排在第1位（最佳选择），而评价得分最低的6号样品排在第13位（最差选择）. 该方法的排序结果表明，该方法的排序性能与以前的方法有显著的一致性，其秩相关系数非常高（Rs>0.87）. 四个虚权集给出的排序模式也具有很高的一致性，秩相关系数大于0.90. 而且，即使初始决策矩阵发生变化，也不会发生秩反转. 因此，基于改变准则权重和动态决策矩阵影响的灵敏度分析进一步增强了该方法在排序性能方面的稳定性和鲁棒性