Sahar Varchandi , Ashkan Memari , Mohammad Reza Akbari Jokar
{"title":"用于选择弹性-可持续供应商的最佳-最差法和模糊 TOPSIS 综合法","authors":"Sahar Varchandi , Ashkan Memari , Mohammad Reza Akbari Jokar","doi":"10.1016/j.dajour.2024.100488","DOIUrl":null,"url":null,"abstract":"<div><p>Achieving a balance between economic, environmental, and social factors in supplier selection while prioritizing business continuity poses a considerable challenge. It is imperative to guarantee that selected suppliers adhere to sustainability and resilience requirements while supporting the company’s economic advancement. This study addresses this challenge through a novel approach that combines the Best–Worst Method (BWM) with the Fuzzy Technique Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Integrating these methodologies reduces the burden of pairwise comparisons, a common challenge in supplier selection using multi-criteria decision-making, thereby streamlining the evaluation process. To assess the effectiveness of the proposed model, we implemented our method on an actual case study of e-commerce and conducted a sensitivity analysis of the results. The findings suggest that the proposed method offers improved consistency in rankings across criteria compared to traditional BWM. It also makes a balance between simplicity and accuracy, ensuring that selected suppliers are equipped to handle disruptions and uncertainties. This aligns practical simplicity with theoretical rigor which makes the proposed method more accessible and manageable for practitioners in real-world settings.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100488"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000924/pdfft?md5=eebd793dba470c62dffccfd7c312e60d&pid=1-s2.0-S2772662224000924-main.pdf","citationCount":"0","resultStr":"{\"title\":\"An integrated best–worst method and fuzzy TOPSIS for resilient-sustainable supplier selection\",\"authors\":\"Sahar Varchandi , Ashkan Memari , Mohammad Reza Akbari Jokar\",\"doi\":\"10.1016/j.dajour.2024.100488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Achieving a balance between economic, environmental, and social factors in supplier selection while prioritizing business continuity poses a considerable challenge. It is imperative to guarantee that selected suppliers adhere to sustainability and resilience requirements while supporting the company’s economic advancement. This study addresses this challenge through a novel approach that combines the Best–Worst Method (BWM) with the Fuzzy Technique Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Integrating these methodologies reduces the burden of pairwise comparisons, a common challenge in supplier selection using multi-criteria decision-making, thereby streamlining the evaluation process. To assess the effectiveness of the proposed model, we implemented our method on an actual case study of e-commerce and conducted a sensitivity analysis of the results. The findings suggest that the proposed method offers improved consistency in rankings across criteria compared to traditional BWM. It also makes a balance between simplicity and accuracy, ensuring that selected suppliers are equipped to handle disruptions and uncertainties. This aligns practical simplicity with theoretical rigor which makes the proposed method more accessible and manageable for practitioners in real-world settings.</p></div>\",\"PeriodicalId\":100357,\"journal\":{\"name\":\"Decision Analytics Journal\",\"volume\":\"11 \",\"pages\":\"Article 100488\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772662224000924/pdfft?md5=eebd793dba470c62dffccfd7c312e60d&pid=1-s2.0-S2772662224000924-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Analytics Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772662224000924\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Analytics Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772662224000924","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An integrated best–worst method and fuzzy TOPSIS for resilient-sustainable supplier selection
Achieving a balance between economic, environmental, and social factors in supplier selection while prioritizing business continuity poses a considerable challenge. It is imperative to guarantee that selected suppliers adhere to sustainability and resilience requirements while supporting the company’s economic advancement. This study addresses this challenge through a novel approach that combines the Best–Worst Method (BWM) with the Fuzzy Technique Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Integrating these methodologies reduces the burden of pairwise comparisons, a common challenge in supplier selection using multi-criteria decision-making, thereby streamlining the evaluation process. To assess the effectiveness of the proposed model, we implemented our method on an actual case study of e-commerce and conducted a sensitivity analysis of the results. The findings suggest that the proposed method offers improved consistency in rankings across criteria compared to traditional BWM. It also makes a balance between simplicity and accuracy, ensuring that selected suppliers are equipped to handle disruptions and uncertainties. This aligns practical simplicity with theoretical rigor which makes the proposed method more accessible and manageable for practitioners in real-world settings.