Supplier selection for carbon emission reduction collaboration in green supply chain using an improved multi-criteria decision-making method

PurposePlatform-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 hes...

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Vydáno v:Asia Pacific journal of marketing and logistics Ročník 36; číslo 8; s. 1918 - 1945
Hlavní autoři: Wang, Qing, Zhang, Xiaoli, Su, Jiafu, Zhang, Na
Médium: Journal Article
Jazyk:angličtina
Vydáno: Patrington Emerald Publishing Limited 30.07.2024
Emerald Group Publishing Limited
Témata:
Approximation
Carbon
Collaboration
Comparative analysis
Emissions control
Fuzzy sets
Literature reviews
Methods
Multiple criteria decision making
Suppliers
Supply chains
Transportation services
Supplier selection
Probabilistic hesitant fuzzy sets
Platform-based enterprises
Multi-criteria decision making
Carbon emission reduction
ISSN:1355-5855, 1758-4248
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Shrnutí:PurposePlatform-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/approachThis 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.FindingsIn 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/valueFirstly, 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.
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ISSN:1355-5855
1758-4248
DOI:10.1108/APJML-11-2023-1084