Research article

A new hybrid MCDM approach for mitigating risks of hazardous material road transportation

  • Received: 13 October 2023 Revised: 24 December 2023 Accepted: 16 January 2024 Published: 26 February 2024
  • Given the ongoing development of the global economy, the demand for hazardous materials, which serve as essential components for numerous industrial products, is steadily increasing. Consequently, it becomes imperative to devise a methodology for mitigating the risks associated with the road transportation of hazardous materials. The objective of this study is to establish an integrated quality function deployment and multicriteria decision-making (QFD-MCDM) framework and identify the pivotal factors that propel Industry 5.0 (I5.0), thus fortifying supply chain resilience (SCR) and ameliorating the hazardous material transportation risks (HMTR). These measures encompass various strategic areas, including "establish a safe and inclusive work environment", "customized products and services", "enhance production flexibility and strengthen control redundancy", and "real-time data collection and analysis". By adopting these measures, enterprises can lead to sustainable and stable business operations. The findings of this study demonstrate the synergistic potential of integrating I5.0 and SCR in effectively mitigating HMTR. Additionally, these findings offer valuable insights and practical implications for enterprises across diverse industries.

    Citation: Chihhung Hsu, Ji Yang, Anyuan Chang, Guohao Liu. A new hybrid MCDM approach for mitigating risks of hazardous material road transportation[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4210-4240. doi: 10.3934/mbe.2024186

    Related Papers:

  • Given the ongoing development of the global economy, the demand for hazardous materials, which serve as essential components for numerous industrial products, is steadily increasing. Consequently, it becomes imperative to devise a methodology for mitigating the risks associated with the road transportation of hazardous materials. The objective of this study is to establish an integrated quality function deployment and multicriteria decision-making (QFD-MCDM) framework and identify the pivotal factors that propel Industry 5.0 (I5.0), thus fortifying supply chain resilience (SCR) and ameliorating the hazardous material transportation risks (HMTR). These measures encompass various strategic areas, including "establish a safe and inclusive work environment", "customized products and services", "enhance production flexibility and strengthen control redundancy", and "real-time data collection and analysis". By adopting these measures, enterprises can lead to sustainable and stable business operations. The findings of this study demonstrate the synergistic potential of integrating I5.0 and SCR in effectively mitigating HMTR. Additionally, these findings offer valuable insights and practical implications for enterprises across diverse industries.



    加载中


    [1] Y. Liu, L. Fan, X. Li, S. Shi, Y. Lu, Trends of hazardous material accidents (HMAs) during highway transportation from 2013 to 2018 in China, J. Loss Prev. Process Ind., 66 (2020), 104150. https://doi.org/10.1016/j.jlp.2020.104150 doi: 10.1016/j.jlp.2020.104150
    [2] A. Ghaderi, R. L. Burdett, An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network, Transp. Res. Part E Logist. Transp. Rev., 127 (2019), 49–65. https://doi.org/10.1016/j.tre.2019.04.011 doi: 10.1016/j.tre.2019.04.011
    [3] S. Ghaleh, M. Omidvari, P. Nassiri, M. Momeni, S. M. M. Lavasani, Pattern of safety risk assessment in road fleet transportation of hazardous materials (oil materials), Saf. Sci., 116 (2019), 1–12. https://doi.org/10.1016/j.ssci.2019.02.039 doi: 10.1016/j.ssci.2019.02.039
    [4] N. Holeczek, Hazardous materials truck transportation problems: A classification and state of the art literature review, Transp. Res. Part D Transp. Environ., 69 (2019), 305–328. https://doi.org/10.1016/j.trd.2019.02.010 doi: 10.1016/j.trd.2019.02.010
    [5] S. S. Mohri, M. Mohammadi, M. Gendreau, A. Pirayesh, A. Ghasemaghaei, V. Salehi, Hazardous material transportation problems: A comprehensive overview of models and solution approaches, Eur. J. Oper. Res., 302 (2022), 1–38. https://doi.org/10.1016/j.ejor.2021.11.045 doi: 10.1016/j.ejor.2021.11.045
    [6] N. Stojanovic, B. Boskovic, M. Petrovic, I. Grujic, O. I. Abdullah, The impact of accidents during the transport of dangerous good, on people, the environment, and infrastructure and measures for their reduction: A review, Environ. Sci. Pollut. Res., 30 (2023), 32288–32300. https://doi.org/10.1007/s11356-023-25470-2 doi: 10.1007/s11356-023-25470-2
    [7] Y. L. Li, Q. Yang, K. S. Chin, A decision support model for risk management of hazardous materials road transportation based on quality function deployment, Transp. Res. Part D Transp. Environ., 74 (2019), 154–173. https://doi.org/10.1016/j.trd.2019.07.026 doi: 10.1016/j.trd.2019.07.026
    [8] H. Ma, X. Li, Y. Liu, Multi-period multi-scenario optimal design for closed-loop supply chain network of hazardous products with consideration of facility expansion, Soft Comput., 24 (2020), 2769–2780. https://doi.org/10.1007/s00500-019-04435-z doi: 10.1007/s00500-019-04435-z
    [9] A. Norrman, A. Wieland, The development of supply chain risk management over time: Revisiting Ericsson, Int. J. Phys. Distrib. Logist. Manage., 50 (2020), 641–666. https://doi.org/10.1108/IJPDLM-07-2019-0219 doi: 10.1108/IJPDLM-07-2019-0219
    [10] A. Wieland, C. F. Durach, Two perspectives on supply chain resilience, J. Bus. Logist., 42 (2021), 315–322. https://doi.org/10.1111/jbl.12271 doi: 10.1111/jbl.12271
    [11] S. Wei, W. Xu, X. Guo, X. Chen, How does business-IT alignment influence supply chain resilience?, Inf. Manage., 60 (2023), 103831. https://doi.org/10.1016/j.im.2023.103831 doi: 10.1016/j.im.2023.103831
    [12] A. Spieske, M. Gebhardt, M. Kopyto, H. Birkel, E. Hartmann, The future of industry 4.0 and supply chain resilience after the COVID-19 pandemic: Empirical evidence from a Delphi study, Comput. Ind. Eng., 181 (2023), 109344. https://doi.org/10.1016/j.cie.2023.109344 doi: 10.1016/j.cie.2023.109344
    [13] G. Qader, M. Junaid, Q. Abbas, M. S. Mubarik, Industry 4.0 enables supply chain resilience and supply chain performance, Technol. Forecast. Soc. Change, 185 (2022), 122026. https://doi.org/10.1016/j.techfore.2022.122026 doi: 10.1016/j.techfore.2022.122026
    [14] R. Sindhwani, S. Afridi, A. Kumar, A. Banaitis, S. Luthra, P. L. Singh, Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers, Technol. Soc., 68 (2022), 101887. https://doi.org/10.1016/j.techsoc.2022.101887 doi: 10.1016/j.techsoc.2022.101887
    [15] European Commission Directorate General for Research and Innovation, Industry 5.0: Towards a sustainable, human centric and resilient European industry, 2021. Available from: https://data.europa.eu/doi/10.2777/308407.
    [16] X. Xu, Y. Lu, B. Vogel-Heuser, L.Wang, Industry, 4.0 and Industry 5.0—inception, conception and perception, J. Manuf. Syst., 61 (2021), 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006 doi: 10.1016/j.jmsy.2021.10.006
    [17] D. Ivanov, The Industry 5.0 framework: Viability-based integration of the resilience, sustainability, and human-centricity perspectives, Int. J. Prod. Res., 61 (2023), 1683–1695. https://doi.org/10.1080/00207543.2022.2118892 doi: 10.1080/00207543.2022.2118892
    [18] M. Z. Mistarihi, R. A. Okour, A. A. Mumani, An integration of a QFD model with fuzzy-ANP approach for determining the importance weights for engineering characteristics of the proposed wheelchair design, Appl. Soft Comput., 90 (2020), 106136. https://doi.org/10.1016/j.asoc.2020.106136 doi: 10.1016/j.asoc.2020.106136
    [19] Y. Chen, Y. Ran, G. Huang, L. Xiao, G. Zhang, A new integrated MCDM approach for improving QFD based on DEMATEL and extended MULTIMOORA under uncertainty environment, Appl. Soft Comput., 105 (2021), 107222. https://doi.org/10.1016/j.asoc.2021.107222 doi: 10.1016/j.asoc.2021.107222
    [20] J. P. Chang, Z. S. Chen, X. J. Wang, L. Martínez, W. Pedrycz, M. J. Skibniewski, Requirement-driven sustainable supplier selection: Creating an integrated perspective with stakeholders' interests and the wisdom of expert crowds, Comput. Ind. Eng., 175 (2023), 108903. https://doi.org/10.1016/j.cie.2022.108903 doi: 10.1016/j.cie.2022.108903
    [21] A. E. Torkayesh, M. Yazdani, D. Ribeiro-Soriano, Analysis of Industry 4.0 implementation in mobility sector: An integrated approach based on QFD, BWM, and stratified combined compromise solution under fuzzy environment, J. Ind. Inf. Integr., 30 (2022), 100406. https://doi.org/10.1016/j.jii.2022.100406 doi: 10.1016/j.jii.2022.100406
    [22] X. Shen, S. Wei, Application of XGBoost for hazardous material road transport accident severity analysis, IEEE Access, 8 (2020), 206806–206819. https://doi.org/10.1109/ACCESS.2020.3037922 doi: 10.1109/ACCESS.2020.3037922
    [23] N. Vojinović, S. Sremac, D. Zlatanović, A novel integrated fuzzy-rough MCDM model for evaluation of companies for transport of dangerous goods, Complexity, 2021 (2021), e5141611. https://doi.org/10.1155/2021/5141611 doi: 10.1155/2021/5141611
    [24] A. Baryłka, M. Chmieliński, Innovative technologies supporting the safety of the transport of dangerous goods, Mod. Eng., 2020 (2020), 3.
    [25] UNECE, Dangerous goods publications. Available from: https://unece.org/publications/transport/dangerous%20goods.
    [26] OTIF, Intergovernmental organisation for international carriage by rail. Available from: https://otif.org/en/.
    [27] Q. Yang, K. S. Chin, Y. L. Li, A quality function deployment-based framework for the risk management of hazardous material transportation process, J. Loss Prev. Process Ind., 52 (2018), 81–92. https://doi.org/10.1016/j.jlp.2018.02.001 doi: 10.1016/j.jlp.2018.02.001
    [28] J. Guo, C. Luo, K. Ma, Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment, Reliability Eng. Syst. Saf., 229 (2023), 108891. https://doi.org/10.1016/j.ress.2022.108891 doi: 10.1016/j.ress.2022.108891
    [29] Z. Yang, X. Yan, Y. Tian, Z Pu, Y Wang, C Li, et al., Risk assessment of sudden water pollution accidents associated with dangerous goods transportation on the cross-tributary bridges of Baiyangdian lake, Water, 15 (2023), 2993. https://doi.org/10.3390/w15162993 doi: 10.3390/w15162993
    [30] F. Ma, D. Yu, B. Xue, X. Wang, J. Jing, W. Zhang, Transport risk modeling for hazardous chemical transport companies—a case study in China, J. Loss Prev. Process Ind., 84 (2023), 105097. https://doi.org/10.1016/j.jlp.2023.105097 doi: 10.1016/j.jlp.2023.105097
    [31] G. Behzadi, M. J. O'Sullivan, T. L. Olsen, On metrics for supply chain resilience, Eur. J. Oper. Res., 287 (2020), 145–158. https://doi.org/10.1016/j.ejor.2020.04.040 doi: 10.1016/j.ejor.2020.04.040
    [32] A. Llaguno, J. Mula, F. Campuzano-Bolarin, State of the art, conceptual framework and simulation analysis of the ripple effect on supply chains, Int. J. Prod. Res., 60 (2022), 2044–2066. https://doi.org/10.1080/00207543.2021.1877842 doi: 10.1080/00207543.2021.1877842
    [33] A. Mohammed, I. Harris, A. Soroka, R. Nujoom, A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design, Comput. Ind. Eng., 127 (2019), 297–312. https://doi.org/10.1016/j.cie.2018.09.052 doi: 10.1016/j.cie.2018.09.052
    [34] S. Hosseini, A. A. Khaled, A hybrid ensemble and AHP approach for resilient supplier selection. J. Intell. Manuf., 30 (2019), 207–228. https://doi.org/10.1007/s10845-016-1241-y doi: 10.1007/s10845-016-1241-y
    [35] H. Zhang, F. Jia, J. X. You, Striking a balance between supply chain resilience and supply chain vulnerability in the cross-border e-commerce supply chain, Int. J. Logist. Res. Appl., 26 (2023), 320–344. https://doi.org/10.1080/13675567.2021.1948978 doi: 10.1080/13675567.2021.1948978
    [36] D. Ozdemir, M. Sharma, A. Dhir, T. Daim, Supply chain resilience during the COVID-19 pandemic, Technol. Soc., 68 (2022), 101847. https://doi.org/10.1016/j.techsoc.2021.101847 doi: 10.1016/j.techsoc.2021.101847
    [37] N. Zhao, J. Hong, K. H. Lau, Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model, Int. J. Prod. Econ., 259 (2023), 108817. https://doi.org/10.1016/j.ijpe.2023.108817 doi: 10.1016/j.ijpe.2023.108817
    [38] J. Liu, J. Wu, Y. Gong, Maritime supply chain resilience: From concept to practice, Comput. Ind. Eng., 182 (2023), 109366. https://doi.org/10.1016/j.cie.2023.109366 doi: 10.1016/j.cie.2023.109366
    [39] W. Liu, Z. Liu, Simulation analysis of supply chain resilience of prefabricated building projects based on system dynamics, Buildings, 13 (2023), 2629. https://doi.org/10.1016/j.acme.2017.04.011 doi: 10.1016/j.acme.2017.04.011
    [40] J. Wang, M. Yu, M. Liu, Influencing factors on green supply chain resilience of agricultural products: An improved gray-DEMATEL-ISM approach, Front. Sustainable Food Syst., 7 (2023). https://doi.org/10.3389/fsufs.2023.1166395 doi: 10.3389/fsufs.2023.1166395
    [41] M. Ghobakhloo, M. Iranmanesh, M. E. Morales, M. Nilashi, A. Amran, Actions and approaches for enabling Industry 5.0‐driven sustainable industrial transformation: A strategy roadmap, Corporate Soc. Responsib. Environ. Manage., 30 (2023), 1473–1494. https://doi.org/10.1002/csr.2431 doi: 10.1002/csr.2431
    [42] M. Sharma, R. Sehrawat, S. Luthra, T. Daim, D. Bakry, Moving towards Industry 5.0 in the pharmaceutical manufacturing sector: Challenges and solutions for Germany, IEEE Trans. Eng. Manage., 2022 (2022), 1–18. https://doi.org/10.1109/TEM.2022.3143466 doi: 10.1109/TEM.2022.3143466
    [43] H. W. Lo, A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing, Adv. Eng. Inf., 56 (2023), 101998. https://doi.org/10.1016/j.aei.2023.101998 doi: 10.1016/j.aei.2023.101998
    [44] S. Nayeri, Z. Sazvar, J. Heydari, Towards a responsive supply chain based on the Industry 5.0 dimensions: A novel decision-making method, Expert Syst. Appl., 213 (2023), 119267. https://doi.org/10.1016/j.eswa.2022.119267 doi: 10.1016/j.eswa.2022.119267
    [45] E. H. Grosse, F. Sgarbossa, C. Berlin, W. P. Neumann, Human-centric production and logistics system design and management: transitioning from Industry 4.0 to Industry 5.0, Int. J. Prod. Res., 61 (2023), 7749–7759. https://doi.org/10.1080/00207543.2023.2246783 doi: 10.1080/00207543.2023.2246783
    [46] D. Ivanov, The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and human-centricity perspectives, Int. J. Prod. Res., 61 (2023), 1683–1695. https://doi.org/10.1080/00207543.2022.2118892 doi: 10.1080/00207543.2022.2118892
    [47] H. R. Soufi, A. Esfahanipour, M. A. Shirazi, Risk reduction through enhancing risk management by resilience, Int. J. Disaster Risk Reduction, 64 (2021), 102497. https://doi.org/10.1016/j.ijdrr.2021.102497 doi: 10.1016/j.ijdrr.2021.102497
    [48] S. W. Chiou, A resilience-based signal control for a time-dependent road network with hazmat transportation, Reliab. Eng. Syst. Saf., 193 (2020), 106570. https://doi.org/10.1016/j.ress.2019.106570 doi: 10.1016/j.ress.2019.106570
    [49] B. Zahiri, N. C. Suresh, J. de Jong, Resilient hazardous-materials network design under uncertainty and perishability, Comput. Ind. Eng., 143 (2020), 106401. https://doi.org/10.1016/j.cie.2020.106401 doi: 10.1016/j.cie.2020.106401
    [50] C. Chen, M. Yang, G. Reniers, A dynamic stochastic methodology for quantifying HAZMAT storage resilience, Reliab. Eng. Syst. Saf., 215 (2021): 107909. https://doi.org/10.1016/j.ress.2021.107909 doi: 10.1016/j.ress.2021.107909
    [51] Z. Wang, Y. Wang, Y. Jiao, Uncertain Multi-objective hazardous materials transport route planning considering resilience and low-carbon, IEEE Access, 11 (2023), 26921–26931. https://doi.org/10.1109/ACCESS.2023.3236796 doi: 10.1109/ACCESS.2023.3236796
    [52] S. Modgil, R. K. Singh, S. Agrawal, Developing human capabilities for supply chains: an industry 5.0 perspective, Ann. Oper. Res., 2023 (2023), 1–31. https://doi.org/10.1007/s10479-023-05245-1 doi: 10.1007/s10479-023-05245-1
    [53] S. Gupta, S. Modgil, T. M. Choi, A. Kumar, J. Antony, Influences of artificial intelligence and blockchain technology on financial resilience of supply chains, Int. J. Prod. Econ., 261 (2023), 108868. https://doi.org/10.1016/j.ijpe.2023.108868 doi: 10.1016/j.ijpe.2023.108868
    [54] D. Ivanov, Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability, Int. J. Prod. Econ., 263 (2023), 108938. https://doi.org/10.1016/j.ijpe.2023.108938 doi: 10.1016/j.ijpe.2023.108938
    [55] B. Naghshineh, H. Carvalho, The implications of additive manufacturing technology adoption for supply chain resilience: A systematic search and review, Int. J. Prod. Econ., 247 (2022), 108387. https://doi.org/10.1016/j.ijpe.2021.108387 doi: 10.1016/j.ijpe.2021.108387
    [56] J. Leng, W. Sha, B. Wang, P. Zheng, C. Zhuang, Q. Liu, et al., Industry 5.0: Prospect and retrospect, J. Manuf. Syst., 65 (2022), 279–295. https://doi.org/10.1016/j.jmsy.2022.09.017 doi: 10.1016/j.jmsy.2022.09.017
    [57] H. R. Fazeli, Q. Peng, Integrated approaches of BWM-QFD and FUCOM-QFD for improving weighting solution of design matrix, J. Intell. Manuf., 34 (2023), 1003–1020. https://doi.org/10.1007/s10845-021-01832-w doi: 10.1007/s10845-021-01832-w
    [58] Y. Luo, M. Ni, F. Zhang, A design model of FBS based on interval-valued Pythagorean fuzzy sets, Adv. Eng. Inf., 56 (2023), 101957. https://doi.org/10.1016/j.aei.2023.101957 doi: 10.1016/j.aei.2023.101957
    [59] L. Ocampo, A. M. Jumao-as, J. J. Labrador, A. M. Rama, Transforming the means-end chain model of the QFD into interconnected hierarchical network structures for sustainable product design, Int. J. Sustainable Eng., 14 (2021), 552–573. https://doi.org/10.1080/19397038.2021.1934182 doi: 10.1080/19397038.2021.1934182
    [60] I. Erol, I. M. Ar, I. Peker, C. Searcy, Alleviating the impact of the barriers to circular economy adoption through blockchain: An investigation using an integrated MCDM-based QFD with hesitant fuzzy linguistic term sets, Comput. Ind. Eng., 165 (2022), 107962. https://doi.org/10.1016/j.cie.2022.107962 doi: 10.1016/j.cie.2022.107962
    [61] M. M. H. Chowdhury, M. A. Quaddus, A multi-phased QFD based optimization approach to sustainable service design, Int. J. Prod. Econ., 171 (2016), 165–178. https://doi.org/10.1016/j.ijpe.2015.09.023 doi: 10.1016/j.ijpe.2015.09.023
    [62] L. Zheng, Z. He, S. He, Detecting and prioritizing product defects using social media data and the two-phased QFD method, Comput. Ind. Eng., 177 (2023), 109031. https://doi.org/10.1016/j.cie.2023.109031 doi: 10.1016/j.cie.2023.109031
    [63] R. Ghlala, Z. K. Aouina, L. B. Said, MC-DMN: Meeting MCDM with DMN involving multi-criteria decision-making in business process, in Computational Science and Its Applications–ICCSA 2017: 17th International Conference, (2017), 3–16. https://doi.org/10.1007/978-3-319-62407-5_1
    [64] M. K. Ghorabaee, M. Amiri, E. K. Zavadskas, J. Antucheviciene, A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations, Arc. Civil Mech. Eng., 18 (2018), 32–49. https://doi.org/10.1016/j.acme.2017.04.011 doi: 10.1016/j.acme.2017.04.011
    [65] N. Vojinović, S. Sremac, D. Zlatanović, A novel integrated fuzzy-rough MCDM model for evaluation of companies for transport of dangerous good, Complexity, 2021 (2021), 1–16. https://doi.org/10.1155/2021/5141611 doi: 10.1155/2021/5141611
    [66] A. H. Sarfaraz, A. K. Yazdi, T. Hanne, R. S. Hosseini, Decision support for technology transfer using fuzzy quality function deployment and a fuzzy inference system, J. Intell. Fuzzy Syst., 44 (2023), 7995–8014. https://doi.org/10.3233/JIFS-222232 doi: 10.3233/JIFS-222232
    [67] G. Büyüközkan, S. Güleryüz, An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey, Int. J. Prod. Econ., 182 (2016), 435–448. https://doi.org/10.1016/j.ijpe.2016.09.015 doi: 10.1016/j.ijpe.2016.09.015
    [68] I. Kazemian, S. A. Torabi, C. W. Zobel, Y. Li, M. Baghersad, A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators, Oper. Res. Int. J., 22 (2022), 1853–1883. https://doi.org/10.1007/s12351-021-00644-3 doi: 10.1007/s12351-021-00644-3
    [69] A. Padilla-Rivera, B. B. T. do Carmo, G. Arcese, N. Merveille, Social circular economy indicators: Selection through fuzzy delphi method, Sustainable Prod. Consumption, 26 (2021), 101–110. https://doi.org/10.1016/j.spc.2020.09.015 doi: 10.1016/j.spc.2020.09.015
    [70] Y. W. Du, X. L. Shen, Group hierarchical DEMATEL method for reaching consensus, Comput. Ind. Eng., 175 (2023), 108842. https://doi.org/10.1016/j.cie.2022.108842 doi: 10.1016/j.cie.2022.108842
    [71] T. L. Saaty, The modern science of multicriteria decision making and its practical applications: The AHP/ANP approach, Oper. Res., 61 (2013), 1101–1118. https://doi.org/10.1287/opre.2013.1197 doi: 10.1287/opre.2013.1197
    [72] S. Daimi, S. Rebai, Sustainability performance assessment of Tunisian public transport companies: AHP and ANP approaches, Socio-Econ. Planning Sci., 89 (2023), 101680. https://doi.org/10.1016/j.seps.2023.101680 doi: 10.1016/j.seps.2023.101680
    [73] Ü. Özdilek, The role of thermodynamic and informational entropy in improving real estate valuation methods, Entropy, 25 (2023), 907. https://doi.org/10.3390/e25060907 doi: 10.3390/e25060907
    [74] P. P. Dwivedi, D. K. Sharma, Evaluation and ranking of battery electric vehicles by Shannon's entropy and TOPSIS methods, Math. Comput. Simul., 212 (2023), 457–474. https://doi.org/10.1016/j.matcom.2023.05.013 doi: 10.1016/j.matcom.2023.05.013
    [75] M. Shakerian, A. Choobineh, M. Jahangiri, M. Alimohammadlou, M. Nami, J. Hasanzadeh, Interactions among cognitive factors affecting unsafe behavior: integrative fuzzy DEMATEL ISM approach, Math. Prob. Eng., 2020 (2020), 1–18. https://doi.org/10.1155/2020/8952624 doi: 10.1155/2020/8952624
    [76] I. Y. Wuni, Mapping the barriers to circular economy adoption in the construction industry: A systematic review, Pareto analysis, and mitigation strategy map, Build. Environ., 223 (2022), 109453. https://doi.org/10.1016/j.buildenv.2022.109453 doi: 10.1016/j.buildenv.2022.109453
    [77] A. Ambituuni, J. M. Amezaga, D. Werner, Risk assessment of petroleum product transportation by road: A framework for regulatory improvement, Saf. Sci., 79 (2015), 324–335. https://doi.org/10.1016/j.ssci.2015.06.022 doi: 10.1016/j.ssci.2015.06.022
    [78] E. Ayyildiz, A. T. Gumus, Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: An application in Istanbul, Environ. Sci. Pollut. Res., 28 (2021), 35798–35810. https://doi.org/10.1007/s11356-021-13223-y doi: 10.1007/s11356-021-13223-y
    [79] A. Belhadi, V. Mani, S. S. Kamble, S. A. R. Khan, S. Verma, Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation, Ann. Oper. Res., 2021 (2021), 1–26. https://doi.org/10.1007/s10479-021-03956-x doi: 10.1007/s10479-021-03956-x
    [80] J. P. Ribeiro, A. Barbosa-Povoa, Supply Chain Resilience: Definitions and quantitative modelling approaches—a literature review, Comput. Ind. Eng., 115 (2018), 109–122. https://doi.org/10.1016/j.cie.2017.11.006 doi: 10.1016/j.cie.2017.11.006
    [81] A. Belhadi, S. S. Kamble, M. Venkatesh, C. J. C. Jabbour, I. Benkhati, Building supply chain resilience and efficiency through additive manufacturing: An ambidextrous perspective on the dynamic capability view, Int. J. Prod. Econ., 249 (2022), 108516. https://doi.org/10.1016/j.ijpe.2022.108516 doi: 10.1016/j.ijpe.2022.108516
    [82] A. Akundi, D. Euresti, S. Luna, W. Ankobiah, A. Lopes, I. Edinbarough, State of Industry 5.0—analysis and identification of current research trends, Appl. Syst. Innovation, 5 (2022), 27. https://doi.org/10.3390/asi5010027 doi: 10.3390/asi5010027
    [83] Y. Lu, H. Zheng, S. Chand, W. Xia, Z. Liu, X. Xu, et al., Outlook on human-centric manufacturing towards Industry 5.0, J. Manuf. Syst., 62 (2022), 612–627. https://doi.org/10.1016/j.jmsy.2022.02.001 doi: 10.1016/j.jmsy.2022.02.001
    [84] M. Kamalahmadi, M. Shekarian, M. M. Parast, The impact of flexibility and redundancy on improving supply chain resilience to disruptions, Int. J. Prod. Res., 60 (2022), 1992–2020. https://doi.org/10.1080/00207543.2021.1883759 doi: 10.1080/00207543.2021.1883759
    [85] R. Lotfi, B. Kargar, M. Rajabzadeh, F. Hesabi, E. Özceylan, Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach, Int. J. Fuzzy Syst., 24 (2022), 1216–1231. https://doi.org/10.1007/s40815-021-01209-4 doi: 10.1007/s40815-021-01209-4
    [86] K. E. K. Vimal, K. Churi, J. Kandasamy, Analysing the drivers for adoption of Industry 4.0 technologies in a functional paper-cement-sugar circular sharing network, Sustainable Prod. Consumption, 31 (2022), 459–477. https://doi.org/10.1016/j.spc.2022.03.006 doi: 10.1016/j.spc.2022.03.006
    [87] C. Jandl, M. Wagner, T. Moser, S. Schlund, Reasons and strategies for privacy features in tracking and tracing systems—a systematic literature review, Sensors, 21 (2021), 4501. https://doi.org/10.3390/s21134501 doi: 10.3390/s21134501
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1031) PDF downloads(77) Cited by(2)

Article outline

Figures and Tables

Figures(3)  /  Tables(17)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog