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Decision support framework for facility location and demand planning for humanitarian logistics

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Abstract

This research purpose is to develop a decision-making mechanism capable of enhancing SADC effectiveness and efficiency during regional relief operations. The research was conducted in three phases: phase 1 with the aim of exploring SADC relief supply operations readiness, their available infrastructures as well as the existing coordination between relief agencies and other relevant parties. Phase 2 aims at enhancing the efficiency and the effectiveness of the SADC regional decision-making mechanism during relief operations. Phase 3 finally optimizes the pre-positioned relief supplies and demands in facility locations across SADC by dealing with two key decision factors affecting the region namely the time and the cost. Using a Multi-Criteria decision Making (MCDM) based on its Analytic Hierarchy Process (AHP) approach, the results, under certain assumptions, yielded an insignificant cost-saving between air transportation and marine transportation. However, applying the decision-making scenarios in an Excel linear optimization model, the study has revealed that by increasing the number of cities or countries involved in a particular humanitarian operation, decreases the overall logistical costs including transportation, prepositioned storage, etc. The proposed methodology provides a detailed decision support framework for humanitarian organizations operating in the region. The model adds important value to the literature as the proposed problem has no solution in the literature before.

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References

  • Africa Infrastructure Country Diagnostic (AICD), (2013) World Bank website, https://data.worldbank.org/data-catalog/africa-infrastructure.

  • Alberto P (2000) The logistics of industrial location decisions: an application of Analytical Hierarchy Process. Int J Logist Res Appl 3(3):273–289

    Article  Google Scholar 

  • Balcik B, Beamon M (2008) Facility location in humanitarian relief. Int J Logist Res Appl 11(2):101–121

    Article  Google Scholar 

  • Baraka JCM, Yadavalli VSS, Dewa M (2018) Disaster relief chains analysis in the SADC region. In: South African Institute of Industrial Engineering Conference Proceedings 29 (1), Spier, Stellenbosch, South Africa.

  • Baraka JCM, Yadavalli VSS, Singh R (2017) A transportation model for an effective disaster relief operation in the SADC region. S Afr J Ind Eng 28(2):46–58

    Google Scholar 

  • Barnhart C, Donald HR (1993) Modelling intermodal routing. J Bus Logist 14(1):205–223

    Google Scholar 

  • Beamon BM, Kotleba SA (2006) Inventory modelling for complex emergencies in humanitarian relief operations. Int J Logist Res Appl 9(1):1–18. https://doi.org/10.1080/13675560500453667

    Article  Google Scholar 

  • Campbell AM, Jones PC (2011) Prepositioning supplies in preparation for disasters. Eur J Oper Res 209(2):156–165

    Article  MathSciNet  Google Scholar 

  • Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655

    Article  Google Scholar 

  • ChenSJ, Hwang CL (1992)Fuzzy multiple attribute decision making: methods and applications. lecture notes in economics and mathematical systems, 375 (1), Sringer-Verlag, Berlin

  • Degener P, Gösling H, Geldermann J (2013) Decision support for the location planning in disaster areas using multi-criteria methods. In: International ISCRAM Conference, vol. 10(1), Baden- Baden, Germany, pp. 278-283

  • Dubois D, Prade H (1980) Fuzzy sets and systems: theory and applications. Academic Press, New York

    MATH  Google Scholar 

  • EM-DAT (2014) The International Disaster Database: https://www.emdat.be/glossary/9#letterd (Access: 13 January 2014).

  • Foster V, Ranganathan R (2011) The SADC's infrastructure: a regional perspective.

  • Habib S, Lee YH, Memon MS (2015) Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review. Hindawi Publishing Corporation. Article in Mathematical Problems in Engineering, 2016 (3212095): 20. https://doi.org/10.1155/2016/3212095.

  • Hale T, Moberg CR (2005) Improving supply chain disaster preparedness: a decision process for secure site location. Int J Phys Distrib Logist Manag 35(3):195–207

    Article  Google Scholar 

  • Kim BS, Opit PF, Lee WS (2011) A stock prepositioning model to maximize the total expected relief demand of disaster areas. Oper Supply Chain Manag 6(2):103–110

    Google Scholar 

  • Kovacs G, Spens KM (2009) Identifying challenges in humanitarian logistics. Int J Physl Distrib Logist Manag 39(6):506–528

    Article  Google Scholar 

  • Masih I, Maskey S, Mussá FEF, Trambauer P (2014) A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol Earth Syst Sci 18(1):3635–3649

    Article  Google Scholar 

  • Moon JH, Kang CS (2011) Application of fuzzy decision-making method to the evaluation of spent fuel storage options. Prog Nucl Energy 39(3):345–351

    Google Scholar 

  • Mpita SN, Yadavalli VSS, Bean WL (2016) Integrated facility location planning and demand assessment for humanitarian logistics: a case study in the Democratic Republic of the Congo. Manag Dyn 25(1):34–50

    Google Scholar 

  • Mulubrhan F, Mokhtar AA, Muhammad M (2014) Comparative analysis between fuzzy and traditional analytical hierarchy process. In: MATEC Web of Conferences, vol. 13 (1), pp. 1-5.

  • Oh SC, Haghani A (1996) Formulation and Solution of multi-commodity, multi-model network flow model for disaster relief operations. Transportation research Part A: Policy and Practice, 1996-Elsevier.

  • Oseni TO, Masarirambi MT (2011) Effect of climate change on maize (Zea mays) production and food security in Swaziland. Am Eurasian J Agric Environ Sci 11(3):385–391

    Google Scholar 

  • Render B, Stair Jr RM, Hanna ME (2006) Quantitative analysis for management, 9th edn. Prentice Hall

  • Roh S, Jang H, Han C (2013) Warehouse location decision factors in humanitarian relief logistics. Asian J Shipping Logist 29(1):103–120

    Article  Google Scholar 

  • Saaty TL (1980) The analytical hierarchy process. McGraw Hill, New York

    MATH  Google Scholar 

  • Strawderman L, Eksioglu B (2009) The role of intermodal transportation in humanitarian supply chains final report. J Emerg Manag 9(2009):25–36

    Google Scholar 

  • Tang Y, Lin T (2011) Application of the fuzzy analytic hierarchy process to the lead-free equipment selection decision. Bus Syst Res 5(1):35–56

    Google Scholar 

  • Tatham P, Houghton L (2011) The wicked problem of humanitarian logistics and disaster relief aid. J Humanit Logist Supply Chain Manag 1(1):15–31

    Article  Google Scholar 

  • Thokala P (2011) Multiple criteria decision analysis for health technology assessment, University of Sheffield, UK, School of Health and Related Research, Sheffield

  • Thomas A (2004) Elevating humanitarian logistics. International Aid and Trade Review Special Edition, 1(1): 102-106

  • Thomas A, Kopczak L (2005) From logistics to supply chain management—the path forward to the humanitarian sector. Fritz Institute, California

    Google Scholar 

  • Timperio G, Panchal GB, Samvedi A, Goh M, De Souza R (2017) Decision support framework for location selection and disaster relief network design. J Humanit Logist Supply Chain Manag 7(3):222–245

    Article  Google Scholar 

  • Triantaphyllou E, Shu B, Sanchez S, Ray T (1998) Multi-criteria decision making: an operations research approach. In: Webster JG (ed) Encyclopedia of Electrical and Electronics Engineering, vol 15(1). John Wiley & Sons, New York, pp 175–186.

  • USAID/OFDA (1996) Disaster history, USAID/OFDA, Washington DC

  • Zhu KJ, Jing Y, Chang DY (1999) A discussion on extent analysis method and applications of fuzzy AHP. Eur J Oper Res 116(3):450–456

    Article  Google Scholar 

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Correspondence to Jean-Claude Baraka Munyaka.

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Munyaka, JC.B., Yadavalli, V.S.S. Decision support framework for facility location and demand planning for humanitarian logistics. Int J Syst Assur Eng Manag 12, 9–28 (2021). https://doi.org/10.1007/s13198-020-01037-z

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  • DOI: https://doi.org/10.1007/s13198-020-01037-z

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