Annals of Operations Research

, Volume 283, Issue 1–2, pp 1303–1343 | Cite as

A multicriteria Master Planning DSS for a sustainable humanitarian supply chain

  • Laura Laguna-Salvadó
  • Matthieu Lauras
  • Uche OkongwuEmail author
  • Tina Comes
S.I.: Applications of OR in Disaster Relief Operations, Part II


Humanitarian supply chains (HSCs) contribute significantly to achieving effective and rapid responses to natural and man-made disasters. Though humanitarian organizations have during the last decades made considerable efforts to improve the response to crises in terms of effectiveness and efficiency, HSCs are still faced with so many challenges, one of which is the incorporation of sustainability dimensions (economic, social and environmental) in the management of their supply chains. In the literature, some authors have highlighted that the planning and achievement of sustainability performance objectives in humanitarian operations is hindered by the lack of decision support systems (DSS). Therefore, this paper proposes a multi-objective Master Planning DSS for managing sustainable HSCs. This Master Planning DSS includes: (1) the definition of a set of metrics for measuring the performance of a sustainable HSC; (2) an algorithm to solve the multi-objective problem; and (3) a Master Planning mathematical model to support the tactical planning of the sustainable HSC. Using the information gathered from field research and the literature, an illustrative numerical example is presented to demonstrate the implementation and utility of the proposed DSS. The results show that the order in which the three sustainability dimensions (economic, social and environmental) are prioritized has some impact on the performance measures. Therefore, it is important to fix a tolerance that would enable to obtain an acceptable balance (trade-off) between the three sustainability objectives, in line with the prioritization choice of the decision maker.


Disaster relief operations Humanitarian supply chain Sustainable supply chain Sustainability Master Planning Multi-objective decision support system 


  1. Abidi, H., de Leeuw, S., & Klumpp, M. (2014). Humanitarian supply chain performance management: A systematic literature review. Supply Chain Management: An International Journal,19(5/6), 592–608.Google Scholar
  2. Absi, N., Dauzère-Pérès, S., Kedad-Sidhoum, S., Penz, B., & Rapine, C. (2013). Discrete Optimization: Lot sizing with carbon emission constraints. European Journal of Operational Research,227(1), 55–61.Google Scholar
  3. Altay, N., & Green, W. G. (2005). OR/MS research in disaster operations management. European Journal of Operational Research,175(1), 475–493.Google Scholar
  4. Anaya-Arenas, A. M., Renaud, J., & Ruiz, A. (2014). Relief distribution networks: A systematic review. Annals of Operations Research,223, 53–79.Google Scholar
  5. Ashby, A., Leat, M., & Hudson-Smith, M. (2012). Making connections: A review of supply chain management and sustainability literature. Supply Chain Management: An International Journal,17(5), 497–516.Google Scholar
  6. Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics Research and Applications,11(2), 101–121.Google Scholar
  7. Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics,126(1), 22–34.Google Scholar
  8. Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems,12(2), 51–63.Google Scholar
  9. Barbarosoğlu, G., & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society,55(1), 43–53.Google Scholar
  10. Baumann, E. (2011). Modèles d’évaluation des performances économique, environnementale et sociale dans les chaînes logistiques (Phd thesis). INSA de Lyon.Google Scholar
  11. Beamon, B. M., & Kotleba, S. A. (2006). Inventory modelling for complex emergencies in humanitarian relief operations. International Journal of Logistics Research and Applications,9(1), 1–18.Google Scholar
  12. Beske, P., & Seuring, S. (2014). Putting sustainability into supply chain management. Supply Chain Management: An International Journal,19(3), 322–331.Google Scholar
  13. Blecken, A. (2010). Supply chain process modelling for humanitarian organizations. International Journal of Physical Distribution & Logistics Management,40(8/9), 675–692.Google Scholar
  14. Bradley, S. P., Hax, A. C., & Magnanti, T. L. (1977). Applied mathematical programming. Reading, MA: Addison-Wesley Publishing Company.Google Scholar
  15. Branke, J. (Ed.). (2008). Multiobjective optimization: Interactive and evolutionary approaches. Berlin: Springer.Google Scholar
  16. Brundtland, G. H. (1987). Report of the World Commission on environment and development: Our common future. Oslo: United Nations.Google Scholar
  17. Cao, C., Li, C., Yang, Q., Liu, Y., & Qu, T. S. (2018). A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. Journal of Cleaner Production,174, 1422–1435.Google Scholar
  18. Carter, C. R., & Easton, P. L. (2011). Sustainable supply chain management: Evolution and future directions. International Journal of Physical Distribution & Logistics Management,41(1), 46–62.Google Scholar
  19. Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management,38(5), 360–387.Google Scholar
  20. Charles, A., & Lauras, M. (2011). An enterprise modelling approach for better optimisation modelling: Application to the humanitarian relief chain coordination problem. OR Spectrum,33(3), 815–841.Google Scholar
  21. Charvériat, C. (2000). Natural disasters in Latin America and the Caribbean: An overview of risk. Rochester, NY: Social Science Research Network.Google Scholar
  22. Chopra, S., & Meindl, P. (2004). Supply chain management: Strategy, planning, and operation. Englewood Cliffs, NJ: Pearson, Prentice-Hall.Google Scholar
  23. Christopher, M. (1992). Logistics and supply chain management. London: Pitman Publishing.Google Scholar
  24. Cosimato, S., & Troisi, O. (2015). Green supply chain management: Practices and tools for logistics competitiveness and sustainability. The DHL case study. The TQM Journal,27(2), 256–276.Google Scholar
  25. D’Amours, S., Rönnqvist, M., & Weintraub, A. (2008). Using operational research for supply chain planning in the forest products industry and paper industry. Information Systems and Operational Research,46, 265–281.Google Scholar
  26. D’Haene, C., Verlinde, S., & Macharis, C. (2015). Measuring while moving (humanitarian supply chain performance measurement—Status of research and current practice. Journal of Humanitarian Logistics and Supply Chain management,5(2), 146–161.Google Scholar
  27. Davis, L. B., Samanlioglu, F., Qu, X., & Root, S. (2013). Inventory planning and coordination in disaster relief efforts. International Journal of Production Economics,141, 561–573.Google Scholar
  28. Day, J. M., Melnyk, S. A., Larson, P. D., Davis, E. W., & Whybark, D. C. (2012). Humanitarian and disaster relief supply chain: A matter of life and death. Journal of Supply Chain Management,48(2), 21–36.Google Scholar
  29. Dubey, R., & Gunasekaran, A. (2016). The sustainable humanitarian supply chain design: Agility, adaptability and alignment. International Journal of Logistics: Research and Applications,19(1), 62–82.Google Scholar
  30. Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Fosso-Wamba, S. (2017a). World class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management,28(2), 332–362.Google Scholar
  31. Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Hazen, B., Giannakis, M., et al. (2017b). Examining the effect of external pressures and organizational culture on shaping performance measurement systems (PMS) for sustainability benchmarking: Some empirical findings. International Journal of Production Economics,193, 63–76.Google Scholar
  32. Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2017c). Green supply chain management: Theoretical framework and further research directions. Benchmarking: An International Journal,24(1), 184–218.Google Scholar
  33. Elkington, J. (1998). Accounting for the triple bottom line. Measuring Business Excellence,2(3), 18–22.Google Scholar
  34. FAO. (2006). Food aid’s intended and unintended consequences (ESA Working Paper 06-05). Rome.Google Scholar
  35. Filho, W. L. (2000). Dealing with misconceptions on the concept of sustainability. International Journal of Sustainability in Higher Education,1(1), 9–19.Google Scholar
  36. Fleischmann, B., Meyr, H., & Wagner, M. (2005). Advanced planning. In H. Stadtler & C. Kilger (Eds.), Supply chain management and advanced planning (pp. 81–106). Springer, Berlin.Google Scholar
  37. Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research,230(2), 201–211.Google Scholar
  38. Gopalakrishnan, K., Yusuf, Y. Y., Musa, A., Abubakar, T., & Ambursa, H. M. (2012). Sustainable supply chain management: A case study of British Aerospace (BAe) systems. International Journal of Production Economics,140, 193–203.Google Scholar
  39. Gralla, E., Goentzal, J., & Fine, C. (2014). Assessing trade-offs among multiple objectives for humanitarian aid delivery using expert preferences. Production and Operations Management,23(6), 978–989.Google Scholar
  40. Gralla, E., Goentzal, J., & Fine, C. (2016). Problem formulation and solution mechanisms: A behavioral study of humanitarian transportation planning. Production and Operations Management,25(1), 22–359.Google Scholar
  41. Green, K. W., Zelbst, P. J., Meacham, J., & Bhadauria, V. S. (2012). Green supply chain management practices: Impact on performance. Supply Chain Management: An International Journal,17(3), 290–305.Google Scholar
  42. Gualandris, J., Klassen, R. D., Vachon, S., & Kalchschmidt, M. (2015). Sustainable evaluation and verification in supply chains: Aligning andleveraging accountability to stakeholders. Journal of Operations Management,38, 1–13.Google Scholar
  43. Gunasekaran, A., Dubey, R., & Singh, S. P. (2016). Flexible sustainable supply chain network design: Current trends, opportunities and future. Global Journal of Flexible Systems Management,17(2), 109–112.Google Scholar
  44. Haavisto, I., & Goentzal, J. (2015). Measuring humanitarian supply chain performance in a multi-goal context. Journal of Humanitarian Logistics and Supply Chain management,5(3), 300–324.Google Scholar
  45. Haavisto, I., & Kovács, G. (2014). Perspectives on sustainability in humanitarian supply chains. Disaster Prevention and Management,23(5), 610–631.Google Scholar
  46. Hart, S. L. (1997). Beyond greening—Strategies for a Sustainable World. Harvard Business Review,75(1), 66–76.Google Scholar
  47. Hausladen, I., & Haas, A. (2013). Considering sustainability in the context of humanitarian logistics. In B. Hellingrath, D. Link, & A. Widera (Eds.), Managing humanitarian supply chains (pp. 314–329). Hamburg: DVV Media Group GmbH.Google Scholar
  48. Hemming, C., Pugh, S., Williams, G., & Blackburn, D. (2004). Strategies for sustainable development: Use of a benchmarking tool to understand relative strengths and weaknesses and identify best practice. Corporate Social Responsibility and Environmental Management,11, 103–113.Google Scholar
  49. Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An International Journal,12(4), 330–353.Google Scholar
  50. Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L. N., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management,31(5), 262–280.Google Scholar
  51. Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation Research Part E: Logistics and Transportation Review,48(1), 2–18.Google Scholar
  52. Huang, Z., Wei, Y.-M., Wang, K., & Liao, H. (2017). Energy economics and climate policy modeling. Annals of Operations Research,255, 1–7.Google Scholar
  53. IFRC. (2010). IFRC strategy 2020. IFRC. Accessed 29 July 2015.Google Scholar
  54. Jabbour, C. J. C., de Sousa, Lopes, Jabbour, A. B., Govindan, K., Pignatti de Freitas, T., Soubihia, D. F., et al. (2016). Barriers to the adoption of green operational practices at Brazilian companies: Effects on green and operational performance. International Journal of Production Research,54(10), 3042–3058.Google Scholar
  55. Jabbour, C. J., Sobreiro, V. A., Lopes de Sousa Jabbour, A. B., de Souza Campos, L. M., Mariano, E. B., & Renwick, D. W. S. (2017). An analysis of the literature on humanitarian logistics and supply chain management: Paving the way for future studies. Annals of Operations Research.
  56. Jaggernath, R., & Khan, Z. (2015). Green supply chain management. World Journal of Entrepreneurship, Management and Sustainable development,11(1), 37–47.Google Scholar
  57. Jahre, M. (2008). The organizational change of logistics in International Federation of the Red Cross and Red Crescent Societies (Case Study). HUMLOG-NET Project.Google Scholar
  58. Jahre, M., & Heigh, I. (2008). Does the current constraints in funding promote failure in humanitarian supply chains? Supply Chain Forum: An International Journal,9(2), 44–55.Google Scholar
  59. Kauder, S., & Meyr, H. (2009). Strategic network planning for an international automotive manufacturer: Balancing flexibility and economic efficiency. OR Spectrum,31, 507–532.Google Scholar
  60. Kiewiet, D. J., & Vos, J. F. J. (2007). Organizational sustainability: A case for formulating a taylor-made definition. Journal of Environmental Assessment Policy and Management,9(1), 1–18.Google Scholar
  61. Kleindorfer, P. R., Singhal, K., & van Wassenhove, L. N. (2005). Sustainable operations management. Production and Operations Management,14(4), 482–492.Google Scholar
  62. Klumpp, M., De Leeuw, S., Regattieri, A., & de Souza, R. (2015). Humanitarian logistics and sustainability. Berlin: Springer.Google Scholar
  63. Kovács, G., & Spens, K. M. (2011). Trends and developments in humanitarian logistics—A gap analysis. International Journal of Physical Distribution & Logistics Management,41(1), 32–45.Google Scholar
  64. Kunz, N., & Gold, S. (2017). Sustainable humanitarian supply chain management—Exploring new theory. International Journal of Logistics: Research and Applications,20(2), 85–104.Google Scholar
  65. Laguna Salvadó, L., Lauras, M., & Comes, T. (2017). Sustainable performance measurement for humanitarian supply chain operations. In Proceedings of the international conference on information systems for crisis response and management (pp. 775–783). Presented at the ISCRAM 2017, Albi.Google Scholar
  66. Maas, K., & Liket, K. (2011). Social impact measurement: classification of methods. In R. L. Burritt, S. Schaltegger, M. Bennett, T. Pohjola & M. Csutora (Eds.), Environmental management accounting and supply chain management (pp. 171–202). Springer, Dordrecht.Google Scholar
  67. Markley, M. J., & Davis, L. (2007). Exploring future competitive advantage through sustainable supply chains. International Journal of Physical Distribution & Logistics Management,37(9), 763–774.Google Scholar
  68. Maryniak, A. (2017). Competitive instruments preferred by customers versus the level of pro-environmental activities in a supply chain. LogForum,13(2), 159–169.Google Scholar
  69. Medina-Borja, A., & Triantis, K. (2014). Modeling social services performance: A four-stage DEA approach to evaluate fundraising efficiency, capacity building, service quality, and effectiveness in the nonprofit sector. Annals of Operations Research,221, 285–307.Google Scholar
  70. Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics,126, 76–84.Google Scholar
  71. Neely, A., Gregory, M., & Platts, K. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operations and Production Management,15, 80–116.Google Scholar
  72. Noham, R., & Tzur, M. (2018). Designing humanitarian supply chains by incorporating actual post-disaster decisions. European Journal of Operational Research,265, 1064–1077.Google Scholar
  73. Oloruntoba, R. (2010). An analysis of the Cyclone Larry emergency relief chain: Some key success factors. International Journal of Production Economics,126, 85–101.Google Scholar
  74. Ozbay, K., & Ozguven, E. (2007). Stochastic humanitarian inventory control model for disaster planning. Transportation Research Record,2022, 63–75.Google Scholar
  75. Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research,129(1), 217–245.Google Scholar
  76. Paulraj, A., Chen, I. J., & Blome, C. (2017). Motives and performance outcomes of sustainable supply chain management practices: A multiple-theoretical perspective. Journal of Business Ethics,145, 239–258.Google Scholar
  77. Pedraza-Martinez, A. J., Stapleton, O., & Van Wassenhove, L. N. (2013). On the use of evidence in humanitarian logistics research. Disasters,37, S51–S67.Google Scholar
  78. Pojasek, R. B. (2012). Understanding sustainability: An organizational perspective. Environmental Quality Management, 21(3), 93–100.Google Scholar
  79. Presley, A., Meade, L., & Sarkis, J. (2007). A strategic sustainability justification methodology for organizational decisions: A reverse logistics illustration. International Journal of Production Research,45(18–19), 4595–4620.Google Scholar
  80. Rastegar, N., & Khorram, E. (2015). Relaxation of constraints in lexicographic multiobjective programming problems. Optimization: A Journal of Mathematical Programming and Operations Research,64(10), 2111–2129.Google Scholar
  81. Rentmeesters, M. J., Tsai, W. K., & Lin, K.-J. (1996). A theory of lexicographic multi-criteria optimization. In Proceedings of the second IEEE international conference on engineering of complex computer systems, 1996 (pp. 76–79).Google Scholar
  82. Richardson, D. A., De Leeuw, S., & Dullaert, W. (2016). Factors affecting global inventory locations in humanitarian operations—a Delphi study. Journal of Business Logistics,37(1), 59–74.Google Scholar
  83. Robins, F. (2006). The challenge of TBL: A responsibility to whom? Business and Society Review,111(1), 1–14.Google Scholar
  84. Rottkemper, B., Fischer, K., Blecken, A., & Danne, C. (2011). Inventory relocation for overlapping disaster settings in humanitarian operations. OR Spectrum,33, 721–749.Google Scholar
  85. Rudberg, M., & Thulin, J. (2009). Centralised supply chain master planning employing advanced planning systems. Production Planning & Control,20(2), 158–167.Google Scholar
  86. Santarelli, G., Abidi, H., Klumpp, A. R., & Regattieri, A. (2015). Humanitarian supply chains and performance measurement schemes in practice. International Journal of Productivity and Performance Measurement,64(6), 784–810.Google Scholar
  87. Sarkis, J., Zhu, Q., & Lai, K-h. (2011). An organizational theoretic review of green supply chain management literature. International Journal of Production Economics,130, 1–15.Google Scholar
  88. Schrettle, S., Hinz, A., Scherrer-Rathje, M., & Friedli, T. (2014). Turning sustainability into action: Explaining firms sustainability efforts and their impact on firm performance. International Journal of Production Economics,147, 73–84.Google Scholar
  89. Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production,16(15), 1699–1710.Google Scholar
  90. Sherali, H. D. (1982). Equivalent weights for lexicographic multi-objective programs: Characterizations and computations. European Journal of Operational Research,11(4), 367–379.Google Scholar
  91. Singh, A., & Trivedi, A. (2016). Sustainable green supply chain management: Trends and current practices. Competitiveness Review,26(3), 265–288.Google Scholar
  92. Stadtler, H. (2005). Supply chain management and advanced planning—Basics, overview and challenges. European Journal of Operational Research,163(3), 575–588.Google Scholar
  93. Stadtler, H., & Kilger, C. (Eds.). (2005). Supply chain management and advanced planning: Concepts, models, software and case studies (3rd ed.). Berlin: Springer.Google Scholar
  94. Tatham, P. H., & Pettit, S. J. (2010). Transforming humanitarian logistics: The journey to supply chain network management. International Journal of Physical Distribution & Logistics Management,40(8/9), 609–622.Google Scholar
  95. Taticchi, P., Garengo, P., Nudurupati, S. S., Tonelli, F., & Pasqualino, R. (2015). A review of decision-support tools and performance management and sustainable supply chain management. International Journal of Production Research,53(21), 6473–6494.Google Scholar
  96. Thomas, A. (2003). Humanitarian logistics: Enabling disaster response. San Francisco: Fritz Institute. Accessed 10 Nov 2016.
  97. Tzur, M. (2016). The humanitarian pickup and distribution problem. Presented at the EuroHope mini-conference, Hamburg. March 5.Google Scholar
  98. Ülkü, M. A., Bell, K. M., & Wilson, S. G. (2015). Modeling the impact of donor behavior on humanitarian aid operations. Annals of Operations Research,230, 153–168.Google Scholar
  99. UN OCHA. (2014). Global Humanitarian Overview (Status Report). Accessed 25 Feb 2015.
  100. United Nations. (2016a). The Sustainable Development Goals Report 2016. (L. Jensen, Ed.). New York: United Nations.Google Scholar
  101. United Nations. (2016b). One humanity: shared responsibilityReport of the Secretary-General for the World Humanitarian Summit.Google Scholar
  102. Van Wassenhove, L. N. (2006). Humanitarian aid logistics: Supply chain management in high gear. Journal of the Operational Research Society,57(5), 475–489.Google Scholar
  103. Vanajakumari, M., Kumar, S., & Gupta, S. (2016). An integrated logistics model for predictable disasters. Production and Operations Management,25(5), 791–811.Google Scholar
  104. Vargas Florez, J., Lauras, M., Okongwu, U., & Dupont, L. (2015). A decision support system for robust humanitarian facility location. Engineering Applications of Artificial Intelligence,46, 326–335.Google Scholar
  105. Vega-Mejía, C. A., Montoya-Torres, J. R., & Islam, S. M. N. (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. Scholar
  106. Vinck, P. (Ed.). (2013). World disasters report 2013: Focus on technology and the future of humanitarian intervention. Geneva: IFRC.Google Scholar
  107. WFP. (2017). Cash-based transfers for delivering food assistance. World Food Programme. Accessed 12 July 2017.
  108. Widera, A., Dietrich, H.-A., Hellingrath, B., & Becker, J. (2013). Understanding humanitarian supply chains—Developing an integrated process analysis toolkit. In 10th International ISCRAM Conference. Presented at the Information Systems for Crisis Response and Management, Baden-Baden.Google Scholar
  109. Wilson, M. (2003). Corporate sustainability: What is it and where does it come from? Ivey Business Journal,67(6), 1–5.Google Scholar
  110. Wray, K. H., Zilberstein, S., & Mouaddib, A.-I. (2015). Multi-objective MDPs with conditional lexicographic reward preferences. In Twenty-ninth AAAI conference on artificial intelligence (pp. 3418–3424). Presented at the AAAI, Austin, Texas, USA: AAAI Press.Google Scholar
  111. Yadav, D., & Barve, A. (2016). Modeling oost-disaster challenges of humanitarian supply chains: A TISM approach. Global Journal of Flexible Systems Management,17(3), 321–340.Google Scholar
  112. Yang, F., Yuan, Q., Du, S., & Liang, L. (2016). Reserving relief supplies for earthquake: A multi-attribute decision making of China Red Cross. Annals of Operations Research,247, 759–785.Google Scholar
  113. Ye, Y., & Liu, N. (2013). Humanitarian logistics planning for natural disaster response with Bayesian information updates. Journal of Industrial and Management Optimization,10(3), 665–689.Google Scholar

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Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity of Toulouse, IMT Mines AlbiAlbi Cedex 9France
  2. 2.Department of Information, Operations and Management SciencesToulouse Business SchoolToulouseFrance
  3. 3.Department of ICTUniversity of AgderGrimstadNorway
  4. 4.Department of Multi-Actor SystemsDelft University of TechnologyDelftThe Netherlands

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