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The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

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  • Athakorn Kengpol
  • Sopida Tuammee

Abstract

The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.

Suggested Citation

  • Athakorn Kengpol & Sopida Tuammee, 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, Taylor & Francis Journals, vol. 54(4), pages 1020-1038, February.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:4:p:1020-1038
    DOI: 10.1080/00207543.2015.1041570
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    Cited by:

    1. Rui Ren & Wanjie Hu & Jianjun Dong & Bo Sun & Yicun Chen & Zhilong Chen, 2019. "A Systematic Literature Review of Green and Sustainable Logistics: Bibliometric Analysis, Research Trend and Knowledge Taxonomy," IJERPH, MDPI, vol. 17(1), pages 1-25, December.
    2. Nikoleta Mikušová & Gabriel Fedorko & Vieroslav Molnár & Martina Hlatká & Rudolf Kampf & Veronika Sirková, 2021. "Possibility of a Solution of the Sustainability of Transport and Mobility with the Application of Discrete Computer Simulation—A Case Study," Sustainability, MDPI, vol. 13(17), pages 1-24, September.
    3. Yuji Sato & Kim Hua Tan, 2023. "Inconsistency indices in pairwise comparisons: an improvement of the Consistency Index," Annals of Operations Research, Springer, vol. 326(2), pages 809-830, July.
    4. Faghih-Roohi, Shahrzad & Akcay, Alp & Zhang, Yingqian & Shekarian, Ehsan & de Jong, Eelco, 2020. "A group risk assessment approach for the selection of pharmaceutical product shipping lanes," International Journal of Production Economics, Elsevier, vol. 229(C).
    5. Huang, Min & Tu, Jun & Chao, Xiuli & Jin, Delong, 2019. "Quality risk in logistics outsourcing: A fourth party logistics perspective," European Journal of Operational Research, Elsevier, vol. 276(3), pages 855-879.
    6. Guoquan Zhang & Guohao Li & Jing Peng, 2020. "Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    7. Ho, William & Ma, Xin, 2018. "The state-of-the-art integrations and applications of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 267(2), pages 399-414.
    8. Mojtaba M. Shourkaei & Kelsey M. Taylor & Bruno Dyck, 2024. "Examining sustainable supply chain management via a social‐symbolic work lens: Lessons from Patagonia," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1477-1496, February.
    9. Jinghan Zhang & Wujun Cao & Minyoung Park, 2019. "Reliability Analysis and Optimization of Cold Chain Distribution System for Fresh Agricultural Products," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    10. K. R. Aswin & V. R. Renjith & K. R. Akshay, 2022. "FMECA using fuzzy logic and grey theory: a comparitve case study applied to ammonia storage facility," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 2084-2103, August.

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