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Evaluating the risk exposure of sustainable freight transportation: a two-phase solution approach

  • S.I. : OR for Sustainability in Supply Chain Management
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A Correction to this article was published on 04 May 2021

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Abstract

A combination of sustainability-related regulations and increased demand from stakeholders has pressed firms to adequately address sustainability-related risks. This issue is particularly important, and challenging, for the freight transportation sector due to its exposure to a large number of inherent sustainability risks. Despite the growing significance of sustainability risks, there is a lack of research related to sustainability risk management, which may be due to the difficulties in identifying and evaluating sustainability risks. We aim to fill this research gap by identifying, measuring and modelling sustainability risks in the context of freight transportation. Our research makes three primary contributions. First, we introduce the concept of a sustainability risk index (SRI) to understand the risk exposure of freight transportation systems (FTSs) in the context of India, an emerging market. The SRI is a mathematical tool used to measure sustainability risks and to quantify a firm’s exposure to sustainability-related risks. Second, we propose an integrated two-phase model based on an interval 2-tuple linguistic model and a digraph matrix approach to calculate the SRI. In contrast to other existing techniques, the proposed approach can effectively deal with uncertain and incomplete linguistic assessments without suffering a loss of information. Third, we propose a framework for calculating both the disruption scores and influencing power of sustainability risks to evaluate the associated criticality and triggering power. For a robustness check, we also conduct a sensitivity analysis of the impact of risk variations on the SRI. Unlike conventional perceptions, our results show that organisational and governmental risks, which are mostly behavioural and skills-induced, are more significant for sustainable FTSs compared to financial risks. Our research helps the managerial community in the freight transportation sector in emerging markets to engage in more informed decision-making to proactively mitigate sustainability risks, which have potentially devastating financial, environmental and societal impacts.

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References

  • Adem, A., Çolak, A., & Dağdeviren, M. (2018). An integrated model using SWOT analysis and Hesitant fuzzy linguistic term set for evaluation occupational safety risks in life cycle of wind turbine. Safety Science, 106, 184–190.

    Google Scholar 

  • Agrawal, S., Singh, R. K., & Murtaza, Q. (2016). Outsourcing decisions in reverse logistics: Sustainable balanced scorecard and graph theoretic approach. Resources, Conservation and Recycling, 108, 41–53.

    Google Scholar 

  • Ali, I. L., Hird, A., Tanko, M., & Whitfield, R. I. (2019). SME approach to road transportation risk management: evidence from Nigeria. In 24th International Conference on Urban Transport and the Environment: Urban Transport 2018 (pp. 39–47).

  • Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195, 106–117.

    Google Scholar 

  • Bai, C., Fahimnia, B., & Sarkis, J. (2017). Sustainable transport fleet appraisal using a hybrid multi-objective decision making approach. Annals of Operations Research, 250(2), 309–340.

    Google Scholar 

  • Brandenburg, M., & Rebs, T. (2015). Sustainable supply chain management: A modelling perspective. Annals of Operations Research, 229(1), 213–252.

    Google Scholar 

  • Busse, C. (2016). Doing well by doing good? The self-interest of buying firms and sustainable supply chain management. Journal of Supply Chain Management, 52(2), 28–47.

    Google Scholar 

  • Busse, C., Schleper, M. C., Weilenmann, J., & Wagner, S. M. (2018). Extending the supply chain visibility boundary: Utilizing stakeholders for identifying supply chain sustainability risks. International Journal of Physical Distribution and Logistics Management, 47(1), 18–40.

    Google Scholar 

  • Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution and Logistics Management, 38(5), 360–387.

    Google Scholar 

  • Chen, C.T., & Tai, W. S. (2005). Measuring the intellectual capital performance based on 2-tuple fuzzy linguistic information. Paper presented at the The 10th Annual Meeting of APDSI, Asia Pacific Region of Decision Sciences Institute.

  • Choi, T. M., Chiu, C. H., & Chan, H. K. (2016). Risk management of logistics systems. Transportation Research Part E: Logistics and Transportation Review, 90, 1–6.

    Google Scholar 

  • Choudhary, D., Shankar, R., & Choudhary, A. (2020). An integrated approach for modeling sustainability risks in freight transportation systems. Risk Analysis, 40(4), 858–883.

    Google Scholar 

  • Cui, J., Dodson, J., & Hall, P. V. (2015). Planning for urban freight transport: An overview. Transport Reviews, 35(5), 583–598.

    Google Scholar 

  • Dadsena, K., Sarmah, S. P., & Naikan, V. N. A. (2019). Risk evaluation and mitigation of sustainable road freight transport operation: A case of trucking industry. International Journal of Production Research, 57(19), 6223–6245.

    Google Scholar 

  • Danloup, N., Mirzabeiki, V., Allaoui, H., Goncalves, G., Julien, D., & Mena, C. (2015). Reducing transportation greenhouse gas emissions with collaborative distribution: A case study. Management Research Review, 38(10), 1049–1067.

    Google Scholar 

  • Demir, E., Huang, Y., Scholts, S., & Van Woensel, T. (2015). A selected review on the negative externalities of the freight transportation: Modeling and pricing. Transportation research part E: Logistics and transportation review, 77, 95–114.

    Google Scholar 

  • Dubey, R., & Gunasekaran, A. (2015). The role of truck driver on sustainable transportation and logistics. Industrial and Commercial Training, 47(3), 127–134.

    Google Scholar 

  • Evangelista, P., Colicchia, C., & Creazza, A. (2017). Is environmental sustainability a strategic priority for logistics service providers? Journal of Environmental Management, 198, 353–362.

    Google Scholar 

  • Qaiser, F. H., Ahmed, K., Sykora, M., Choudhary, A., & Simpson, M. (2017). Decision support systems for sustainable logistics: A review and bibliometric analysis. Industrial Management and Data Systems, 117(7), 1376–1388.

    Google Scholar 

  • Fahimnia, B., Tang, C. S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research, 247(1), 1–15.

    Google Scholar 

  • Gan, L. (2003). Globalization of the automobile industry in China: Dynamics and barriers in greening of the road transportation. Energy Policy, 31(6), 537–551.

    Google Scholar 

  • Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171, 455–470.

    Google Scholar 

  • Goldman, T., & Gorham, R. (2006). Sustainable urban transport: Four innovative directions. Technology in Society, 28(1–2), 261–273.

    Google Scholar 

  • Govindan, K., & Chaudhuri, A. (2016). Interrelationships of risks faced by third party logistics service providers: A DEMATEL based approach. Transportation Research Part E: Logistics and Transportation Review, 90, 77–95.

    Google Scholar 

  • Hajmohammad, S., & Vachon, S. (2016). Mitigation, avoidance, or acceptance? Managing supplier sustainability risk. Journal of Supply Chain Management, 52(2), 48–65.

    Google Scholar 

  • Harclerode, M. A., Macbeth, T. W., Miller, M. E., Gurr, C. J., & Myers, T. S. (2016). Early decision framework for integrating sustainable risk management for complex remediation sites: Drivers, barriers, and performance metrics. Journal of Environmental Management, 184, 57–66.

    Google Scholar 

  • Herrera, F., & Martínez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746–752.

    Google Scholar 

  • Herrera, F., & Martínez, L. (2001). A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 31(2), 227–234.

    Google Scholar 

  • Hofmann, H., Busse, C., Bode, C., & Henke, M. (2014). Sustainability-related supply chain risks: Conceptualization and management. Business Strategy and the Environment, 23(3), 160–172.

    Google Scholar 

  • KPMG. (2007). Skill gaps in the Indian logistics sector: a white paper. Confederation of Indian Industry. http://www.in.kpmg.com/pdf/logistics.pdf. Accessed on 16th August, 2019.

  • Kengpol, A., & Tuammee, S. (2016). The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: An empirical study. International journal of production research, 54(4), 1020–1038.

    Google Scholar 

  • Lee, H. L., & Tang, C. S. (2018). Socially and environmentally responsible value chain innovations: New operations management research opportunities. Management Science, 64(3), 983–996.

    Google Scholar 

  • Lee, S. Y., & Klassen, R. D. (2008). Drivers and enablers that foster environmental management capabilities in small-and medium-sized suppliers in supply chains. Production and Operations Management, 17(6), 573–586.

    Google Scholar 

  • Li, H., You, J. X., Liu, H. C., & Tian, G. (2018). Acquiring and sharing tacit knowledge based on interval 2-tuple linguistic assessments and extended fuzzy Petri nets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(01), 43–65.

    Google Scholar 

  • Lindholm, M. (2010). A sustainable perspective on urban freight transport: Factors affecting local authorities in the planning procedures. Paper presented at the Procedia - Social and Behavioral Sciences.

  • Liu, H. C., You, J. X., Chen, S., & Chen, Y. Z. (2016a). An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty. IEE Transactions, 48(11), 1027–1042.

    Google Scholar 

  • Liu, H. C., Ren, M. L., Wu, J., & Lin, Q. L. (2014a). An interval 2-tuple linguistic MCDM method for robot evaluation and selection. International Journal of Production Research, 52(10), 2867–2880.

    Google Scholar 

  • Liu, H. C., You, J. X., & You, X. Y. (2014b). Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure. Computers and Industrial Engineering, 78, 249–258.

    Google Scholar 

  • Liu, H. C., You, J. X., Li, P., & Su, Q. (2016b). Failure mode and effect analysis under uncertainty: An integrated multiple criteria decision making approach. IEEE Transactions on Reliability, 65(3), 1380–1392.

    Google Scholar 

  • Liu, H. C., You, J. X., Shan, M. M., & Su, Q. (2019). Systematic failure mode and effect analysis using a hybrid multiple criteria decision-making approach. Total Quality Management and Business Excellence, 30(5–6), 537–564.

    Google Scholar 

  • Liu, H. C., Chen, Y. Z., You, J. X., & Li, H. (2016c). Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach. Journal of Intelligent Manufacturing, 27(4), 805–816.

    Google Scholar 

  • Muduli, K., Govindan, K., Barve, A., & Geng, Y. (2013). Barriers to green supply chain management in Indian mining industries: A graph theoretic approach. Journal of Cleaner Production, 47, 335–344.

    Google Scholar 

  • Meinlschmidt, J., Schleper, M. C., & Foerstl, K. (2018). Tackling the sustainability iceberg: A transaction cost economics approach to lower tier sustainability management. International Journal of Operations and Production Management. https://doi.org/10.1108/IJOPM-03-2017-0141

    Article  Google Scholar 

  • Nguyen, S., Chen, P. S. L., Du, Y., & Shi, W. (2019). A quantitative risk analysis model with integrated deliberative Delphi platform for container shipping operational risks. Transportation Research Part E: Logistics and Transportation Review, 129, 203–227.

    Google Scholar 

  • NITI Aayog. (2018). Goods on the move. Global Mobility Summit. https://niti.gov.in/writereaddata/files/document_publication/Freight_report.pdf, Accessed on 17th September, 2018.

  • Paladugula, A. L., Kholod, N., Chaturvedi, V., Ghosh, P. P., Pal, S., Clarke, L., et al. (2018). A multi-model assessment of energy and emissions for India’s transportation sector through 2050. Energy Policy, 116, 10–18.

    Google Scholar 

  • Piecyk, M. I., & McKinnon, A. C.(2010). Forecasting the carbon footprint of road freight transport in 2020.International Journal of Production Economics, 128 (1), 31–42.

  • Rajesh, R., Ravi, V., & Venkata Rao, R. (2015). Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches. International Journal of Production Research, 53(1), 238–257.

    Google Scholar 

  • Reefke, H., & Sundaram, D. (2017). Key themes and research opportunities in sustainable supply chain management—identification and evaluation. Omega, 66, 195–211.

    Google Scholar 

  • Reinerth, D., Busse, C., & Wagner, S. M. (2019). Using country sustainability risk to inform sustainable supply chain management: A design science study. Journal of Business Logistics, 40(3), 241–264.

    Google Scholar 

  • Richardson, B. C. (2005). Sustainable transport: Analysis frameworks. Journal of Transport Geography, 13, 29–39.

    Google Scholar 

  • Sanchez-Rodrigues, P. V. A., & Naim, M. M. (2010). The impact of logistics uncertainty on sustainable transport operations. International Journal of Physical Distribution and Logistics Management, 40(1–2), 61–83.

    Google Scholar 

  • Shafiq, A., Johnson, P. F., Klassen, R. D., & Awaysheh, A. (2017). Exploring the implications of supply risk on sustainability performance. International Journal of Operations and Production Management. https://doi.org/10.1108/IJOPM-01-2016-0029

    Article  Google Scholar 

  • Shankar, R., Choudhary, D., & Jharkharia, S. (2018). An integrated risk assessment model: A case of sustainable freight transportation systems. Transportation Research Part D: Transport and Environment, 63, 662–676.

    Google Scholar 

  • Soni, U., Jain, V., & Kumar, S. (2014). Measuring supply chain resilience using a deterministic modeling approach. Computers and Industrial Engineering, 74(1), 11–25.

    Google Scholar 

  • Song, W., Ming, X., & Liu, H. C. (2017). Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method. Journal of Cleaner Production, 143, 100–115.

    Google Scholar 

  • Stasko, T. H., & Oliver Gao, H. (2012). Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation. Transportation Research Part A: Policy and Practice, 46(8), 1216–1226.

    Google Scholar 

  • SteadieSeifi, M., Dellaert, N. P., Nuijten, W., Van Woensel, T., & Raoufi, R. (2014). Multimodal freight transportation planning: A literature review. European Journal of Operational Research, 233(1), 1–15.

    Google Scholar 

  • Stephenson, J., Spector, S., Hopkins, D., & McCarthy, A. (2018). Deep interventions for a sustainable transport future. Transportation Research Part D: Transport and Environment, 61, 356–372.

    Google Scholar 

  • Torres-Ruiz, A., & Ravindran, A. R. (2018). Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. Journal of Cleaner Production, 172, 4478–4493.

    Google Scholar 

  • Vega-Mejía, C. A., Montoya-Torres, J. R., & Islam, S. M. (2017). Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: A systematic literature review. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2723-9

    Article  Google Scholar 

  • Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics, 126(1), 121–129.

    Google Scholar 

  • Wan, C., Yan, X., Zhang, D., Qu, Z., & Yang, Z. (2019). An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks. Transportation Research Part E: Logistics and Transportation Review, 125, 222–240.

    Google Scholar 

  • Wang, Y., Sanchez Rodrigues, V., & Evans, L. (2015). The use of ICT in road freight transport for CO2 reduction—An exploratory study of UK’s grocery retail industry. The International Journal of Logistics Management, 26(1), 2–29.

    Google Scholar 

  • WEO. India Energy Outlook. International Energy Agency, http://www.iea.org/publications/freepublications/publication/IndiaEnergyOutlook_WEO2015.pdf. Accessed 7th September 2018.

  • Wu, Z., Xu, J., & Xu, Z. (2016). A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centers. Annals of Operations Research, 247(2), 735–757.

    Google Scholar 

  • Zhang, H. (2012). The multiattribute group decision making method based on aggregation operators with interval-valued 2-tuple linguistic information. Mathematical and Computer Modelling, 56(1–2), 27–35.

    Google Scholar 

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Acknowledgements

We thank Dr. Atanu Chaudhuri, Associate Professor, Business School, Durham University for assisting us in this research paper. The authors would also like to acknowledge the funding support received by UK-India Education Research Initiative (UKIERI) and University Grant Commission (UGC) for project titled “Advanced Analytics for Green and Resilient Supply Chain Decision Making.”

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Authors and Affiliations

  1. Indian Institute of Management Sambalpur, Sambalpur, Odisha, India

    Divya Choudhary

  2. School of Business and Economics, Loughborough University, Loughborough, UK

    Alok Choudhary

  3. Department of Management Studies, Indian Institute of Technology, Delhi, New-Delhi, India

    Ravi Shankar

  4. Newcastle University Business School, Newcastle, NE1 7RU, UK

    Christian Hicks

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  1. Divya Choudhary
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  2. Alok Choudhary
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Appendices

Appendix 1

$$\stackrel{\sim }{{RM}_{FI}} = \left[\begin{array}{ccccc}& r5& r6& r7& r8\\ r5&\Delta [\mathrm{0.660,0.700}]&\Delta [\mathrm{0.920,0.960}]&\Delta [0, 0]&\Delta [\mathrm{0.480,0.520}]\\ r6&\Delta [\mathrm{0.520,0.560}]&\Delta [\mathrm{0.600,0.666}]&\Delta [\mathrm{0.720,0.720}]&\Delta [0.600, 0.680]\\ r7&\Delta [\mathrm{0.520,0.60}0]&\Delta [0.440, 0.520]&\Delta [\mathrm{0.866,0.900}]&\Delta [\mathrm{0.720,0.720}]\\ r8&\Delta [0.640, 0.720]&\Delta [0.760, 0.800]&\Delta [\mathrm{0.840,0.920}]&\Delta [\mathrm{0.533,0.600}]\end{array}\right]$$
(36)
$$\stackrel{\sim }{{RM}_{IN}=}\left[\begin{array}{cccc}& r9& r10& r11\\ r9&\Delta [\mathrm{0.766,0.833}]&\Delta [\mathrm{0,0}]&\Delta [\mathrm{0.800,0.880}]\\ r10&\Delta [\mathrm{0.880,0.920}]&\Delta [0.600, 0.666]&\Delta [\mathrm{0.920,0.960}]\\ r11&\Delta [\mathrm{0.760,0.840}]&\Delta [\mathrm{0,0}]&\Delta [\mathrm{0.766,0.800}]\end{array}\right]$$
(37)
$$\stackrel{\sim }{{RM}_{E\&S }}= \left[\begin{array}{ccccccccc}& r12& r13& r14& r15& r16& r17& r18& r19\\ r12&\Delta \left[\mathrm{0.900,0.966}\right]&\Delta \left[\mathrm{0,0}\right]&\Delta \left[0.680, 0.720\right]&\Delta \left[0.600, 0.640\right]&\Delta \left[0.840, 0.920\right]&\Delta \left[0.640, 0.720\right]&\Delta \left[0, 0\right]&\Delta \left[\mathrm{0,0}\right]\\ r13&\Delta \left[0.800, 0.880\right]&\Delta \left[\mathrm{0.633,0.666}\right]&\Delta \left[0.920, 0.960\right]&\Delta \left[0.640, 0.720\right]&\Delta \left[0.840, 0.880\right]&\Delta \left[0.080, 0.120\right]&\Delta \left[0, 0\right]&\Delta \left[0.320, 0.400\right]\\ r14&\Delta \left[0.920, 0.960\right]&\Delta \left[0.240, 0.360\right]&\Delta \left[\mathrm{0.933,0.966}\right]&\Delta \left[0.520, 0.560\right]&\Delta \left[0.880, 0.920\right]&\Delta \left[0.360, 0.400\right]&\Delta \left[0.160, 0.200\right]&\Delta \left[0.280, 0.320\right]\\ r15&\Delta \left[0.400, 0.480\right]&\Delta \left[0.280, 0.360\right]&\Delta \left[0.480, 0.520\right]&\Delta \left[\mathrm{0.633,0.700}\right]&\Delta \left[0.640, 0.720\right]&\Delta \left[0.600, 0.640\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]\\ r16&\Delta \left[0, 0\right]&\Delta \left[0.200, 0.240\right]&\Delta \left[0, 0\right]&\Delta \left[0.160, 0.200\right]&\Delta \left[\mathrm{0.800,0.833}\right]&\Delta \left[0, 0\right]&\Delta \left[0.280, 0.320\right]&\Delta \left[0, 0\right]\\ r17&\Delta \left[0, 0\right]&\Delta \left[0.920, 0.960\right]&\Delta \left[0.360, 0.440\right]&\Delta \left[0.720, 0.760\right]&\Delta \left[0.880, 0.920\right]&\Delta \left[0.600, 0.666\right]&\Delta \left[0, 0\right]&\Delta \left[0.560, 0.640\right]\\ r18&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0.680, 0.720\right]&\Delta \left[0, 0\right]&\Delta \left[0.666, 0.733\right]&\Delta \left[0.560, 0.600\right]\\ r19&\Delta \left[0.520, 0.600\right]&\Delta \left[0.680, 0.680\right]&\Delta \left[0.600, 0.640\right]&\Delta \left[0.640, 0.640\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0, 0\right]&\Delta \left[0.600, 0.640\right]&\Delta \left[0.800, 0.866\right]\end{array}\right]$$
(38)
$$\stackrel{\sim }{{RM}_{MR}} =\left[\begin{array}{ccccc}& r20& r21& r22& r23\\ r20&\Delta [\mathrm{0.633,0.700}]&\Delta [\mathrm{0.280,0.320}]&\Delta [0.800, 0.800]&\Delta [\mathrm{0.600,0.640}]\\ r21&\Delta [\mathrm{0,0}]&\Delta [\mathrm{0.366,0.433}]&\Delta [\mathrm{0,0}]&\Delta [0.560, 0.600]\\ r22&\Delta [\mathrm{0.320,0.360}]&\Delta [0.800, 0.840]&\Delta [\mathrm{0.666,0.666}]&\Delta [\mathrm{0.560,0.640}]\\ r23&\Delta [0.600, 0.600]&\Delta [0.800, 0.840]&\Delta [\mathrm{0.680,0.760}]&\Delta [\mathrm{0.666,0.700}]\end{array}\right]$$
(39)
$$\stackrel{\sim }{{RM}_{OP}} =\left[\begin{array}{ccccccccc}& r24& r25& r26& r27& r28& r29& r30& r31\\ r24&\Delta \left[\mathrm{0.766,0.800}\right]&\Delta \left[0.640, 0.720\right]&\Delta \left[0.240, 0.320\right]&\Delta \left[0.520, 0.560\right]&\Delta \left[0.720, 0.760\right]&\Delta \left[\mathrm{0,0}\right]&\Delta \left[0.280, 0.320\right]&\Delta \left[\mathrm{0.760,0.840}\right]\\ r25&\Delta \left[0.800, 0.880\right]&\Delta \left[\mathrm{0.800,0.866}\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.16, 0.20\right]&\Delta \left[0.520, 0.520\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0.840, 0.880\right]\\ r26&\Delta \left[0.600, 0.640\right]&\Delta \left[0, 0\right]&\Delta \left[\mathrm{0.900,0.933}\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]\\ r27&\Delta \left[0.920, 0.960\right]&\Delta \left[0.760, 0.840\right]&\Delta \left[0.600, 0.600\right]&\Delta \left[\mathrm{0.700,0.766}\right]&\Delta \left[0.200, 0.240\right]&\Delta \left[0.200, 0.240\right]&\Delta \left[0, 0\right]&\Delta \left[0.560, 0.640\right]\\ r28&\Delta \left[0.640, 0.680\right]&\Delta \left[0.400, 0.400\right]&\Delta \left[0.560, 0.600\right]&\Delta \left[0, 0\right]&\Delta \left[\mathrm{0.533,0.566}\right]&\Delta \left[0.400, 0.440\right]&\Delta \left[0.280, 0.360\right]&\Delta \left[0.400, 0.400\right]\\ r29&\Delta \left[0.440, 0.480\right]&\Delta \left[0.640, 0.680\right]&\Delta \left[0.640, 0.680\right]&\Delta \left[0.240, 0.280\right]&\Delta \left[0.320, 0.360\right]&\Delta \left[\mathrm{0.733,0.833}\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0, 0\right]\\ r30&\Delta \left[0.600, 0.640\right]&\Delta \left[0, 0\right]&\Delta \left[0.840, 0.880\right]&\Delta \left[0.360, 0.400\right]&\Delta \left[0.560, 0.640\right]&\Delta \left[0.560, 0.560\right]&\Delta \left[\mathrm{0.500,0.566}\right]&\Delta \left[0, 0\right]\\ r31&\Delta \left[0.880, 0.880\right]&\Delta \left[0.800, 0.800\right]&\Delta \left[0.680, 0.720\right]&\Delta \left[0.280, 0.320\right]&\Delta \left[0.600, 0.600\right]&\Delta \left[0.400, 0.480\right]&\Delta \left[0, 0\right]&\Delta \left[\mathrm{0.733,0.766}\right]\end{array}\right]$$
(40)
$$\stackrel{\sim }{{RM}_{O\&G}} =\left[\begin{array}{cccccc}& r32& r33& r34& r35& r36\\ r32&\Delta \left[0.800, 0.833\right]&\Delta \left[0.480, 0.520\right]&\Delta \left[0, 0\right]&\Delta \left[0.640, 0.680\right]&\Delta \left[0.800, 0.880\right]\\ r33&\Delta \left[0.840, 0.920\right]&\Delta \left[0.833, 0.833\right]&\Delta \left[0.560, 0.640\right]&\Delta \left[0.520, 0.560\right]&\Delta \left[0.920, 0.920\right]\\ r34&\Delta \left[0.920, 0.960\right]&\Delta \left[0.720, 0.800\right]&\Delta \left[0.933, 0.966\right]&\Delta \left[0.400, 0.480\right]&\Delta \left[0.880, 0.920\right]\\ r35&\Delta \left[0, 0\right]&\Delta \left[0.200, 0.240\right]&\Delta \left[0, 0\right]&\Delta \left[0.733, 0.766\right]&\Delta \left[0.600, 0.600\right]\\ r36&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0, 0\right]&\Delta \left[0.400, 0.400\right]&\Delta \left[0.866, 0.933\right]\end{array}\right]$$
(41)

Appendix 2

$$per\left(\stackrel{\sim }{{RRM}^{I}}\right)=\left[\begin{array}{cccccccc}& FM& FI& IN& E\&S& MR& OP& O\&G\\ FM&\Delta \left[\mathrm{0.421,0.657}\right]&\Delta \left[0.840, 0.880\right]&\Delta \left[0.600, 0.640\right]&\Delta \left[0.960, 1.00\right]&\Delta \left[0.480, 0.480\right]&\Delta \left[\mathrm{0.800,0.880}\right]&\Delta \left[0.720, 0.800\right]\\ FI&\Delta \left[0.760, 0.840\right]&\Delta \left[1.1612, 1.534\right]&\Delta \left[0.680, 0.760\right]&\Delta \left[0.480, 0.480\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.680, 0.720\right]&\Delta \left[0.600, 0.600\right]\\ IN&\Delta \left[0.920, 0.960\right]&\Delta \left[0.560, 0.640\right]&\Delta \left[\mathrm{0,0}\right]&\Delta \left[0.760, 0.800\right]&\Delta \left[0.760, 0.800\right]&\Delta \left[0.920, 0.920\right]&\Delta \left[0.560, 0.600\right]\\ E\&S&\Delta \left[0.360, 0.440\right]&\Delta \left[0.800, 0.880\right]&\Delta \left[0.280, 0.320\right]&\Delta \left[\mathrm{2.450,5.149}\right]&\Delta \left[0.640, 0.680\right]&\Delta \left[0.720, 0.800\right]&\Delta \left[0.800, 0.800\right]\\ MR&\Delta \left[0.280, 0.320\right]&\Delta \left[0.400, 0.400\right]&\Delta \left[0, 0\right]&\Delta \left[0.560, 0.640\right]&\Delta \left[\mathrm{0.364,0.440}\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.520, 0.600\right]\\ OP&\Delta \left[0.880, 0.960\right]&\Delta \left[0.800, 0.880\right]&\Delta \left[0.560, 0.600\right]&\Delta \left[0.680, 0.760\right]&\Delta \left[0.320, 0.360\right]&\Delta \left[\mathrm{1.668,3.166}\right]&\Delta \left[0.680, 0.760\right]\\ O\&G&\Delta \left[0.800, 0.800\right]&\Delta \left[0.720, 0.800\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.880, 0.920\right]&\Delta \left[0.400, 0.480\right]&\Delta \left[0.760, 0.800\right]&\Delta \left[\mathrm{0.092,0.129}\right]\end{array}\right]=\Delta [\mathrm{252,752}]$$
(42)
$$per\left(\stackrel{\sim }{{RRM}^{W}}\right)=\left[\begin{array}{cccccccc}& FM& FI& IN& E\&S& MR& OP& O\&G\\ FM&\Delta \left[\mathrm{3.637,4.479}\right]&\Delta \left[0.840, 0.880\right]&\Delta \left[0.600, 0.640\right]&\Delta \left[0.960, 1.00\right]&\Delta \left[0.480, 0.480\right]&\Delta \left[\mathrm{0.800,0.880}\right]&\Delta \left[0.720, 0.800\right]\\ FI&\Delta \left[0.760, 0.840\right]&\Delta \left[6.038, 7.172\right]&\Delta \left[0.680, 0.760\right]&\Delta \left[0.480, 0.480\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.680, 0.720\right]&\Delta \left[0.600, 0.600\right]\\ IN&\Delta \left[0.920, 0.960\right]&\Delta \left[0.560, 0.640\right]&\Delta \left[\mathrm{1.608,1.739}\right]&\Delta \left[0.760, 0.800\right]&\Delta \left[0.760, 0.800\right]&\Delta \left[0.920, 0.920\right]&\Delta \left[0.560, 0.600\right]\\ E\&S&\Delta \left[0.360, 0.440\right]&\Delta \left[0.800, 0.880\right]&\Delta \left[0.280, 0.320\right]&\Delta \left[\mathrm{46.961,77.768}\right]&\Delta \left[0.640, 0.680\right]&\Delta \left[0.720, 0.800\right]&\Delta \left[0.800, 0.800\right]\\ MR&\Delta \left[0.280, 0.320\right]&\Delta \left[0.400, 0.400\right]&\Delta \left[0, 0\right]&\Delta \left[0.560, 0.640\right]&\Delta \left[3.607, 4.08\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.520, 0.600\right]\\ OP&\Delta \left[0.880, 0.960\right]&\Delta \left[0.800, 0.880\right]&\Delta \left[0.560, 0.600\right]&\Delta \left[0.680, 0.760\right]&\Delta \left[0.320, 0.360\right]&\Delta \left[59.120, 86.619\right]&\Delta \left[0.680, 0.760\right]\\ O\&G&\Delta \left[0.800, 0.800\right]&\Delta \left[0.720, 0.800\right]&\Delta \left[0.800, 0.840\right]&\Delta \left[0.880, 0.920\right]&\Delta \left[0.400, 0.480\right]&\Delta \left[0.760, 0.800\right]&\Delta \left[3.069, 3.597\right]\end{array}\right] =\Delta [1792407, 8402152]$$
(43)

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Choudhary, D., Choudhary, A., Shankar, R. et al. Evaluating the risk exposure of sustainable freight transportation: a two-phase solution approach. Ann Oper Res (2021). https://doi.org/10.1007/s10479-021-03992-7

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