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An enterprise modelling approach for better optimisation modelling: application to the humanitarian relief chain coordination problem

Author

Listed:
  • Aurelie Charles

    (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris])

  • Matthieu Lauras

    (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris])

Abstract

Humanitarian supply chains (HSC) can be considered a new research area. The number of applied scientific publications has considerably increased over the past 15 years. About half of this research work uses quantitative techniques as optimisation decision-support systems. But due to the recentness of this academic area, researchers are finding it difficult to develop accurate, and above all, reliable mathematical models to support their steps towards improvement. This is particularly true concerning the crucial problems of coordination in HSCs. This paper tackles the issue by developing an original quantitative modelling support method. Based on enterprise modelling methodologies, we propose a business process modelling approach that helps in understanding, analysing, evaluating and then developing the formal expression of an HSC. Such a model, therefore, clearly has an added value for practitioners and should enable relevant quantitative models to be produced. Finally, an application on the emergency response processes of the International Federation of Red Cross is detailed in order to validate the relevance and the applicability of our proposal. This experiment allows all the variables and parameters that should be useful for improving the efficiency of the network to be identified.

Suggested Citation

  • Aurelie Charles & Matthieu Lauras, 2011. "An enterprise modelling approach for better optimisation modelling: application to the humanitarian relief chain coordination problem," Post-Print hal-01685415, HAL.
    • Handle: RePEc:hal:journl:hal-01685415
    DOI: 10.1007/s00291-011-0255-2
    Note: View the original document on HAL open archive server: https://hal.science/hal-01685415
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    Citations

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    Cited by:

    1. Laura Laguna-Salvadó & Matthieu Lauras & Uche Okongwu & Tina Comes, 2019. "A multicriteria Master Planning DSS for a sustainable humanitarian supply chain," Annals of Operations Research, Springer, vol. 283(1), pages 1303-1343, December.
    2. Timperio, Giuseppe & Kundu, Tanmoy & Klumpp, Matthias & de Souza, Robert & Loh, Xiu Hui & Goh, Kelvin, 2022. "Beneficiary-centric decision support framework for enhanced resource coordination in humanitarian logistics: A case study from ASEAN," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    3. Feng Yang & Qianqian Yuan & Shaofu Du & Liang Liang, 2016. "Reserving relief supplies for earthquake: a multi-attribute decision making of China Red Cross," Annals of Operations Research, Springer, vol. 247(2), pages 759-785, December.
    4. Christian Wankmüller & Gerald Reiner, 2020. "Coordination, cooperation and collaboration in relief supply chain management," Journal of Business Economics, Springer, vol. 90(2), pages 239-276, March.
    5. Abhishek Behl & Pankaj Dutta, 2019. "Humanitarian supply chain management: a thematic literature review and future directions of research," Annals of Operations Research, Springer, vol. 283(1), pages 1001-1044, December.
    6. Jihai Zhang & Zhile Wang & Fan Ren, 2019. "Optimization of humanitarian relief supply chain reliability: a case study of the Ya’an earthquake," Annals of Operations Research, Springer, vol. 283(1), pages 1551-1572, December.
    7. Devendra K. Yadav & Akhilesh Barve, 2019. "Prioritization of cyclone preparedness activities in humanitarian supply chains using fuzzy analytical network process," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(2), pages 683-726, June.
    8. Kunz, Nathan & Reiner, Gerald & Gold, Stefan, 2014. "Investing in disaster management capabilities versus pre-positioning inventory: A new approach to disaster preparedness," International Journal of Production Economics, Elsevier, vol. 157(C), pages 261-272.
    9. Krittiya Saksrisathaporn & Abdelaziz Bouras & Napaporn Reeveerakul & Aurelie Charles, 2016. "Application of a Decision Model by Using an Integration of AHP and TOPSIS Approaches within Humanitarian Operation Life Cycle," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 887-918, July.
    10. Hossein Baharmand & Diego Vega & Matthieu Lauras & Tina Comes, 2022. "A methodology for developing evidence-based optimization models in humanitarian logistics," Annals of Operations Research, Springer, vol. 319(1), pages 1197-1229, December.
    11. Oscar Rodríguez-Espíndola, 2023. "Two-stage stochastic formulation for relief operations with multiple agencies in simultaneous disasters," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 477-523, June.

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