Social sustainability can be mentioned as one of the pivotal objectives towards sustainable development which has received the least attention comparing to environmental and economic dimensions during these past years. Due to its impact on organization’s competitive power, researchers have proposed models to measure social performance in supply chains. However, most of these researches reveal shortcomings once encountering the cases with a huge number of criteria due to their complex computations. In order to fill this gap, this study proposes a new soft computing multi-criteria interval-valued Pythagorean fuzzy distance to ideal solution approach based on interval-valued Pythagorean closeness which performs outstandingly in cases with a high fluctuation in the number of criteria. A new mechanism is defined to distinguish the weak performing social factors through supply chains by classifying them into four categorize based on their performance and distance to the best performing factors. This approach is unique in the sense that it both covers a remarkable amount of uncertainty and eases the computational processes of the previous multi-criteria decision making approaches by modifying the steps to select the most ideal solution. The feasibility and applicability of this approach have been validated by applications to a numerical case and comparative analysis.

社会可持续性可以说是实现可持续发展的关键目标之一，与过去几年相比，与环境和经济方面的关注相比，社会可持续性受到的关注最少。由于它影响组织的竞争能力，研究人员提出了模型来衡量供应链中的社会绩效。然而，这些研究大多数揭示了一旦遇到具有大量标准的案例，就会由于其复杂的计算而产生缺陷。为了填补这一空白，本研究提出了一种新的软计算多准则间隔值毕达哥拉斯模糊距离到理想解的方法，该方法基于间隔值毕达哥拉斯接近性在标准数量波动很大的情况下表现出色。定义了一种新的机制，通过将供应链中绩效较弱的社会因素根据绩效和与最佳绩效因数的距离分为四类，从而区分供应链中绩效较弱的社会因素。这种方法的独特之处在于，它既涵盖了巨大的不确定性，又通过修改步骤以选择最理想的解决方案，简化了以前的多标准决策方法的计算过程。该方法的可行性和适用性已通过在数值案例和比较分析中的应用得到了验证。这种方法的独特之处在于，它既涵盖了巨大的不确定性，又通过修改步骤以选择最理想的解决方案，简化了以前的多标准决策方法的计算过程。该方法的可行性和适用性已通过在数值案例和比较分析中的应用得到了验证。这种方法的独特之处在于，它既涵盖了巨大的不确定性，又通过修改步骤以选择最理想的解决方案，简化了以前的多标准决策方法的计算过程。该方法的可行性和适用性已通过在数值案例和比较分析中的应用得到了验证。