The selection of sustainable supplier is an extremely important for sustainable supply chain management (SSCM). The assessment process of sustainable supplier selection is a complicated task for decision experts due to involvement of several qualitative and quantitative criteria. As the uncertainty is commonly occurred in sustainable supplier selection problem and hesitant fuzzy set (HFS), an improvement of Fuzzy Set (FS), has been proved as one of the efficient and superior ways to express the uncertain information arisen in practical problems. The present study proposes a novel framework based on COPRAS (Complex Proportional Assessment) method and SWARA (Step-wise Weight Assessment Ratio Analysis) approach to evaluate and select the desirable sustainable supplier within the HFSs context. In the proposed method, an extended SWARA method is employed for determining the criteria weights based on experts’ preferences. Next, to illustrate the efficiency and practicability of the proposed methodology, an empirical case study of sustainable supplier selection problem is taken under Hesitant Fuzzy (HF) environment. Further, sensitivity analysis is performed to check the stability of the presented methodology. At last, a comparison with existing methods is conducted to verify the strength of the obtained result. The final outcomes confirm that the developed framework is more consistent and powerful than other existing approaches.

中文翻译：

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可持续供应商选择的犹豫模糊 SWARA-复杂比例评估方法 (HF-SWARA-COPRAS)

可持续供应商的选择对于可持续供应链管理（SSCM）来说是极其重要的。由于涉及多个定性和定量标准，可持续供应商选择的评估过程对于决策专家来说是一项复杂的任务。由于不确定性在可持续供应商选择问题和犹豫模糊集（HFS）中普遍存在，模糊集（FS）的改进已被证明是表达实际问题中出现的不确定信息的有效和优越的方法之一。本研究提出了一种基于 COPRAS（复杂比例评估）方法和 SWARA（逐步权重评估比率分析）方法的新框架，以在 HFS 环境中评估和选择理想的可持续供应商。在提出的方法中，采用扩展的 SWARA 方法根据专家的偏好确定标准权重。接下来，为了说明所提出方法的效率和实用性，在犹豫模糊（HF）环境下对可持续供应商选择问题进行了实证案例研究。此外，还进行了敏感性分析以检查所提出方法的稳定性。最后，与现有方法进行比较，以验证所得结果的强度。最终结果证实，开发的框架比其他现有方法更加一致和强大。可持续供应商选择问题的实证案例研究是在犹豫模糊（HF）环境下进行的。此外，还进行了敏感性分析以检查所提出方法的稳定性。最后，与现有方法进行比较，以验证所得结果的强度。最终结果证实，开发的框架比其他现有方法更加一致和强大。可持续供应商选择问题的实证案例研究是在犹豫模糊（HF）环境下进行的。此外，还进行了敏感性分析以检查所提出方法的稳定性。最后，与现有方法进行比较，以验证所得结果的强度。最终结果证实，开发的框架比其他现有方法更加一致和强大。