A multicriteria Master Planning DSS for a sustainable humanitarian supply chain
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.
KeywordsDisaster relief operations Humanitarian supply chain Sustainable supply chain Sustainability Master Planning Multi-objective decision support system
- Baumann, E. (2011). Modèles d’évaluation des performances économique, environnementale et sociale dans les chaînes logistiques (Phd thesis). INSA de Lyon.Google Scholar
- Bradley, S. P., Hax, A. C., & Magnanti, T. L. (1977). Applied mathematical programming. Reading, MA: Addison-Wesley Publishing Company.Google Scholar
- Branke, J. (Ed.). (2008). Multiobjective optimization: Interactive and evolutionary approaches. Berlin: Springer.Google Scholar
- Brundtland, G. H. (1987). Report of the World Commission on environment and development: Our common future. Oslo: United Nations.Google Scholar
- Charvériat, C. (2000). Natural disasters in Latin America and the Caribbean: An overview of risk. Rochester, NY: Social Science Research Network.Google Scholar
- Chopra, S., & Meindl, P. (2004). Supply chain management: Strategy, planning, and operation. Englewood Cliffs, NJ: Pearson, Prentice-Hall.Google Scholar
- Christopher, M. (1992). Logistics and supply chain management. London: Pitman Publishing.Google Scholar
- 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.CrossRefGoogle Scholar
- FAO. (2006). Food aid’s intended and unintended consequences (ESA Working Paper 06-05). Rome.Google Scholar
- Hart, S. L. (1997). Beyond greening—Strategies for a Sustainable World. Harvard Business Review, 75(1), 66–76.Google Scholar
- 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
- 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.CrossRefGoogle Scholar
- IFRC. (2010). IFRC strategy 2020. IFRC. Accessed 29 July 2015.Google Scholar
- 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.CrossRefGoogle Scholar
- 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. https://doi.org/10.1007/s10479-017-2536-x.
- 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
- Klumpp, M., De Leeuw, S., Regattieri, A., & de Souza, R. (2015). Humanitarian logistics and sustainability. Berlin: Springer.Google Scholar
- 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
- Pojasek, R. B. (2012). Understanding sustainability: An organizational perspective. Environmental Quality Management, 21(3), 93–100.Google Scholar
- 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
- Stadtler, H., & Kilger, C. (Eds.). (2005). Supply chain management and advanced planning: Concepts, models, software and case studies (3rd ed.). Berlin: Springer.Google Scholar
- Thomas, A. (2003). Humanitarian logistics: Enabling disaster response. San Francisco: Fritz Institute. http://www.fritzinstitute.org/pdfs/whitepaper/enablingdisasterresponse.pdf. Accessed 10 Nov 2016.
- Tzur, M. (2016). The humanitarian pickup and distribution problem. Presented at the EuroHope mini-conference, Hamburg. March 5.Google Scholar
- UN OCHA. (2014). Global Humanitarian Overview (Status Report). https://www.unocha.org/sites/unocha/files/Global%20Humanitarian%20Overview%20Final%2022%20Aug%202014.pdf. Accessed 25 Feb 2015.
- United Nations. (2016a). The Sustainable Development Goals Report 2016. (L. Jensen, Ed.). New York: United Nations.Google Scholar
- United Nations. (2016b). One humanity: shared responsibility—Report of the Secretary-General for the World Humanitarian Summit.Google Scholar
- 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. https://doi.org/10.1007/s10479-017-2723-9.Google Scholar
- Vinck, P. (Ed.). (2013). World disasters report 2013: Focus on technology and the future of humanitarian intervention. Geneva: IFRC.Google Scholar
- WFP. (2017). Cash-based transfers for delivering food assistance. World Food Programme. www.wfp.org/content/2017-cash-based-transfers-fact-sheet. Accessed 12 July 2017.
- 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
- Wilson, M. (2003). Corporate sustainability: What is it and where does it come from? Ivey Business Journal, 67(6), 1–5.Google Scholar
- 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