Purpose

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

Design/methodology/approach

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

Findings

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Originality/value

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

中文翻译：

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改进的多准则决策方法选择绿色供应链碳减排协作供应商

目的

平台企业作为平台经济中的微观主体，具有有效推动供应链供需双方低碳发展的潜力。因此，本文旨在提供一种概率犹豫模糊环境下的多准则决策方法，协助平台型企业选择绿色供应链减碳合作供应商。

设计/方法论/途径

本文结合概率犹豫模糊集（PHFS）的优点来解决不确定性问题，提出一种改进的多准则决策方法PHFS-DNMEREC-MABAC，用于帮助平台型企业选择绿色供应中的碳减排协作供应商链。在这种决策方法中，我们通过直接标准化概率犹豫模糊元素来增强 DNMEREC 和 MABAC 方法的标准化过程。此外，还引入了概率分裂算法来处理不同长度的概率犹豫模糊元素，从而减轻了传统方法在根据风险偏好添加值时容易引入的信息偏差。

发现

在本文中，我们将所提出的方法应用于天猫商城碳减排合作供应商选择的案例研究，并将其与现有的最新决策方法进行比较。结果证明了该方法的适用性以及所引入的概率分裂算法在避免信息偏差方面的有效性。

原创性/价值

首先，本文提出了一种新的多准则决策方法，用于帮助平台型企业选择绿色供应链中的碳减排协作供应商。其次，在该方法中，我们提供了一种处理概率犹豫模糊决策信息的新标准方法。最后引入概率分裂算法，避免在处理概率犹豫模糊元长度不一致的过程中出现信息偏差。