Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
Page(s) 4681 - 4700
DOI https://doi.org/10.1051/ro/2024160
Published online 30 October 2024
  • S. Aria, S.A. Torabi and S. Nayeri, A hybrid fuzzy decision-making approach to select the best online-taxis business. Adv. Ind. Eng. 54 (2020) 99–120. [Google Scholar]
  • B. Esmaeilian, J. Sarkis, K. Lewis and S. Behdad, Blockchain for the future of sustainable supply chain management in Industry 4.0. Resour. Conserv. Recycl. 163 (2020) 105064. [CrossRef] [Google Scholar]
  • A. Fathi, B. Karimi and R.F. Saen, Sustainability assessment of supply chains by a novel robust two-stage network DEA model: A case study in the transport industry. Soft Comput. 26 (2022) 1–18. [Google Scholar]
  • A.A. ForouzeshNejad, Leagile and sustainable supplier selection problem in the Industry 4.0 era: A case study of the medical devices using hybrid multi-criteria decision making tool. Environ. Sci. Pollut. Res. 30 (2023) 13418–13437. [Google Scholar]
  • S. Guo and H. Zhao, Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl. Based Syst. 121 (2017) 23–31. [CrossRef] [Google Scholar]
  • A.D. Guritno, M. Ushada, N.E. Kristanti, M.S. Dharmawati and N.A.S. Putro, Development of quality evaluation model for supply chain of capture fisheries in Southern Coast of Java. Agric. Eng. Int. CIGR J. 23 (2021). [Google Scholar]
  • A. Gyarmathy, K. Peszynski and L. Young, Theoretical framework for a local, agile supply chain to create innovative product closer to end-user: Onshore-offshore debate. Oper. Supply Chain Manag. Int. J. 13 (2020) 108–122. [CrossRef] [Google Scholar]
  • U. Hasson, S.A. Nastase and A. Goldstein, Direct fit to nature: An evolutionary perspective on biological and artificial neural networks. Neuron 105 (2020) 416–434. [CrossRef] [PubMed] [Google Scholar]
  • A. Hosseini Dolatabad, J. Heidary Dahooie, J. Antucheviciene, M. Azari and S.H. Razavi Hajiagha, Supplier selection in the industry 4.0 era by using a fuzzy cognitive map and hesitant fuzzy linguistic VIKOR methodology. Environ. Sci. Pollut. Res. 30 (2023) 52923–52942. [CrossRef] [Google Scholar]
  • D. Ivanov and A. Dolgui, Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58 (2020) 2904–2915. [CrossRef] [Google Scholar]
  • B. Javan-Molaei, R. Tavakkoli-Moghaddam, M. Ghanavati-Nejad and A. Asghari-Asl, A data-driven robust decision-making model for configuring a resilient and responsive relief supply chain under mixed uncertainty. Ann. Oper. Res. (2024) 1–38. [Google Scholar]
  • R.T. Khameneh, M. Elyasi, O. Ö. Özener and A. Ekici, A non-clustered approach to platelet collection routing problem. Comput. Oper. Res. 160 (2023) 106366. [CrossRef] [Google Scholar]
  • M.M. Khan, I. Bashar, G.M. Minhaj, A.I. Wasi and N.U.I. Hossain, Resilient and sustainable supplier selection: An integration of SCOR 4.0 and machine learning approach. Sustain. Resil. Infras. 8 (2023) 1–17. [Google Scholar]
  • M. Krstić, V. Elia, G.P. Agnusdei, F. De Leo, S. Tadić and P.P. Miglietta, Evaluation of the agri-food supply chain risks: The circular economy context. Br. Food J. 126 (2024) 113–133. [CrossRef] [Google Scholar]
  • S. Kusi-Sarpong, H. Gupta, S.A. Khan, C.J. Chiappetta Jabbour, S.T. Rehman and H. Kusi-Sarpong, Sustainable supplier selection based on Industry 4.0 initiatives within the context of circular economy implementation in supply chain operations. Prod. Plan. Control 34 (2023) 999–1019. [CrossRef] [Google Scholar]
  • B. Li, C. Delpha, D. Diallo and A. Migan-Dubois, Application of artificial neural networks to photovoltaic fault detection and diagnosis: A review. Renew. Sustain. Energy Rev. 138 (2021) 110512. [CrossRef] [Google Scholar]
  • S. Nayeri, M.A. Khoei, M.R. Rouhani-Tazangi, M. GhanavatiNejad, M. Rahmani and E.B. Tirkolaee, A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system. Eng. Appl. Artif. Intell. 124 (2023) 106511. [CrossRef] [Google Scholar]
  • S. Nessari, M. Ghanavati-Nejad, F. Jolai, A. Bozorgi-Amiri and S. Rajabizadeh, A data-driven decision-making approach for evaluating the projects according to resilience, circular economy and Industry 4.0 dimension. Eng. Appl. Artif. Intell. 134 (2024) 108608. [CrossRef] [Google Scholar]
  • D. Oliveira-Dias, J. Moyano-Fuentes and J.M. Maqueira-Marín, Understanding the relationships between information technology and lean and agile supply chain strategies: A systematic literature review. Ann. Oper. Res. 312 (2022) 973–1005. [CrossRef] [Google Scholar]
  • Z. Pang, F. Niu and Z. O’Neill, Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons. Renew. Energy 156 (2020) 279–289. [CrossRef] [Google Scholar]
  • M. Qin, C.W. Su, M. Umar, O.R. Lobont¸ and A.G. Manta, Are climate and geopolitics the challenges to sustainable development? Novel evidence from the global supply chain. Econ. Anal. Policy 77 (2023) 748–763. [CrossRef] [Google Scholar]
  • S. Rahimi, A. Hafezalkotob, S.M. Monavari, A. Hafezalkotob and R. Rahimi, Sustainable landfill site selection for municipal solid waste based on a hybrid decision-making approach: Fuzzy group BWM-MULTIMOORA-GIS. J. Clean. Prod. 248 (2020) 119186. [CrossRef] [Google Scholar]
  • A. Rasmussen, H. Sabic, S. Saha and I.E. Nielsen, Supplier selection for aerospace & defense industry through MCDM methods. Clean. Eng. Technol. 12 (2023) 100590. [CrossRef] [Google Scholar]
  • M. Razeghi, A. Hajinezhad, A. Naseri, Y. Noorollahi and S.F. Moosavian, Multi-criteria decision-making for selecting a solar farm location to supply energy to reverse osmosis devices and produce freshwater using GIS in Iran. Sol. Energy 253 (2023) 501–514. [CrossRef] [Google Scholar]
  • J. Rezaei, T. Nispeling, J. Sarkis and L. Tavasszy, A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J. Clean. Prod. 135 (2016) 577–588. [CrossRef] [Google Scholar]
  • O. Rostami, M. Tavakoli, A. Tajally and M. GhanavatiNejad, A goal programming-based fuzzy best–worst method for the viable supplier selection problem: A case study. Soft Comput. 27 (2023) 2827–2852. [CrossRef] [PubMed] [Google Scholar]
  • R.B. Sánchez-Flores, S.E. Cruz-Sotelo, S. Ojeda-Benitez and M.E. Ramírez-Barreto, Sustainable supply chain management—a literature review on emerging economies. Sustainability 12 (2020) 6972. [CrossRef] [Google Scholar]
  • M.S. Sangari, J. Razmi and S. Zolfaghari, Developing a practical evaluation framework for identifying critical factors to achieve supply chain agility. Measurement 62 (2015) 205–214. [CrossRef] [Google Scholar]
  • Z. Sazvar, M. Tavakoli, M. Ghanavati-Nejad and S. Nayeri, Sustainable-resilient supplier evaluation for high-consumption drugs during COVID-19 pandemic using a data-driven decision-making approach. Scientia Iranica (2022). [Google Scholar]
  • Y. Shao, D. Barnes and C. Wu, Sustainable supplier selection and order allocation for multinational enterprises considering supply disruption in COVID-19 era. Aust. J. Manag. 48 (2022) 031289622110669. [Google Scholar]
  • Y. Shen and K. Liao, An application of analytic hierarchy process and entropy weight method in food cold chain risk evaluation model. Front. Psychol. 13 (2022) 825696. [CrossRef] [Google Scholar]
  • S. Srhir, A. Jaegler and J.R. Montoya-Torres, Uncovering Industry 4.0 technology attributes in sustainable supply chain 4.0: A systematic literature review. Bus. Strategy Environ. 32 (2023) 4143–4166. [CrossRef] [Google Scholar]
  • M. Tavakoli, A. Tajally, M. Ghanavati-Nejad and F. Jolai, A Markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem. Soft Comput. 27 (2023) 1–32. [Google Scholar]
  • O.F. Valilai and M. Sodachi, Inspiration of Industry 4.0 to enable a proactive sustainability assessment model through the supply chain. Procedia Manuf. 52 (2020) 356–362. [CrossRef] [Google Scholar]
  • L. Xin, S. Lang and A.R. Mishra, Evaluate the challenges of sustainable supply chain 4.0 implementation under the circular economy concept using new decision making approach. Oper. Manag. Res. 15 (2022) 1–20. [CrossRef] [Google Scholar]
  • P. You, S. Guo, H. Zhao and H. Zhao, Operation performance evaluation of power grid enterprise using a hybrid BWM-TOPSIS method. Sustainability 9 (2017) 2329. [CrossRef] [Google Scholar]
  • S.I. Zaman, S. Khan, S.A.A. Zaman and S.A. Khan, A grey decision-making trial and evaluation laboratory model for digital warehouse management in supply chain networks. Decis. Anal. J. 8 (2023) 100293. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.