Production
https://prod.org.br/article/doi/10.1590/0103-6513.20210070
Production
Research Article

Food aid supply and distribution in insecure regions: world food programme operation analysis in Ethiopia

Adriana Leiras; Paulo Gonçalves; Bervery Chawaguta; Irineu de Brito Junior; Hugo Tsugunobu Yoshida Yoshizaki

Downloads: 0
Views: 695

Abstract

Paper aims: This paper analyzes the food aid supply and distribution for famine relief by the World Food Programme (WFP) in Ethiopia. Food insecurity has increasingly affected people around the world. Furthermore, the characteristics of the provision of humanitarian aid in insecure regions pose several additional challenges over traditional distribution planning (e.g., corruption, losses in last-mile distribution, security escorts).

Originality: Most previous studies addressing famine relief are qualitative, with only a handful including mathematical modeling as this work. Furthermore, we validate our mathematical model with data from a real problem setting.

Research method: We propose a stochastic transshipment network flow model to ensure the efficient allocation of limited resources.

Main findings: The results show high potential for cost savings and offer managerial insights to humanitarian logisticians on the food aid supply and distribution. Our findings indicate that policies in humanitarian logistics should focus on: (a) relaxing legislation for customs clearance of humanitarian supplies; (b) strengthening local market to increase local procurement; (c) implementing tools to enhance security; and (d) monitoring distribution to mitigate the impact of corruption.

Implications for theory and practice: The results suggest that optimizing food aid distribution in Ethiopia can save millions of people vulnerable to malnutrition.

Keywords

Humanitarian logistics. Food aid supply. Food aid distribution. Stochastic optimization. Famine relief.

References

Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255(1), 187-206. http://dx.doi.org/10.1016/j.ejor.2016.04.041.

Angelis, V., Mecoli, M., Nikoi, C., & Storchi, G. (2007). Multiperiod integrated routing and scheduling of World Food Programme cargo planes in Angola. Computers & Operations Research, 34(6), 1601-1615. http://dx.doi.org/10.1016/j.cor.2005.07.012.

Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transport Systems, 12(2), 51-63. http://dx.doi.org/10.1080/15472450802023329.

Balcik, B., Iravani, S., & Smilowitz, K. (2014). Multi-vehicle sequential resource allocation for a nonprofit distribution system. IIE Transactions, 46(12), 1279-1297. http://dx.doi.org/10.1080/0740817X.2013.876240.

Barbarosoǧlu, G., & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. The Journal of the Operational Research Society, 55(1), 43-53. http://dx.doi.org/10.1057/palgrave.jors.2601652.

Baskaya, S., Ertem, M. A., & Duran, S. (2017). Pre-positioning of relief items in humanitarian logistics considering lateral transhipment opportunities. Socio-Economic Planning Sciences, 57, 50-60. http://dx.doi.org/10.1016/j.seps.2016.09.001.

Belgasmi, D. (2007). Emergency operations: Darfur, a case study. Refugee Survey Quarterly, 26(4), 243-249. http://dx.doi.org/10.1093/rsq/hdi0286.

Benini, A. A. (1993). Simulation of the effectiveness of protection and assistance for victims of armed conflit (Sepavac): an example from Mali, West Africa. Journal of Contingencies and Crisis Management, 1(4), 215-228. http://dx.doi.org/10.1111/j.1468-5973.1993.tb00113.x.

Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming. New York: Springer Science & Business Media. http://dx.doi.org/10.1007/978-1-4614-0237-4.

Brito Junior, I., Leiras, A., & Yoshizaki, H. T. Y. (2020). A multi-criteria stochastic programming approach for pre-positioning disaster relief supplies in Brazil. Production, 30, e20200042. http://dx.doi.org/10.1590/0103-6513.20200042.

Caunhye, A. M., Li, M., & Nie, X. (2015). A location-allocation model for casualty response planning during catastrophic radiological incidents. Socio-Economic Planning Sciences, 50, 32-44. http://dx.doi.org/10.1016/j.seps.2015.02.001.

Caunhye, A. M., Zhang, Y., Li, M., & Nie, X. (2016). A location-routing model for prepositioning and distributing emergency supplies. Transportation Research Part E, Logistics and Transportation Review, 90, 161-176. http://dx.doi.org/10.1016/j.tre.2015.10.011.

Chakravarty, A. K. (2014). Humanitarian relief chain: rapid response under uncertainty. International Journal of Production Economics, 151, 146-157. http://dx.doi.org/10.1016/j.ijpe.2013.10.007.

Chander, V., & Shear, L. (2009). An analysis of World Food Programme operations in the Somali region of Ethiopia (Maters thesis) Massachusetts Institute of Technology, Boston.

Charles, A., Lauras, M., Van Wassenhove, L. N., & Dupont, L. (2016). Designing an efficient humanitarian supply network. Journal of Operations Management, 47–48(1), 58-70. http://dx.doi.org/10.1016/j.jom.2016.05.012.

Clark, A., & Culkin, B. (2013). A network transshipment model for planning humanitarian relief operations after a natural disaster. In B. Vitoriano, J. Montero & D. Ruan (Eds.), Decision aid models for disaster management and emergencies (pp. 233-257). Paris: Atlantis Press. http://dx.doi.org/10.2991/978-94-91216-74-9_11.

Condeixa, L. D., Leiras, A., Oliveira, F., & Brito Junior, I. (2017). Disaster relief supply pre-positioning optimization: a risk analysis via shortage mitigation. International Journal of Disaster Risk Reduction, 25, 238-247. http://dx.doi.org/10.1016/j.ijdrr.2017.09.007.

Dantzig, G. (1955). Linear programming under uncertainty. Management Science, 50(12), 1764-1769.

Davis, L. B., Sengul, I., Ivy, J. S., Brock, L. G., & Miles, L. (2014). Scheduling food bank collections and deliveries to ensure food safety and improve access. Socio-Economic Planning Sciences, 48(3), 175-188.

Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27-41. http://dx.doi.org/10.1016/j.ijpe.2017.02.006.

Doocy, S., Sirois, A., Anderson, J., Tileva, M., Biermann, E., Storey, J. D., & Burnham, G. (2011). Food security and humanitarian assistance among displaced Iraqi populations in Jordan and Syria. Social Science & Medicine, 72(2), 273-282. http://dx.doi.org/10.1016/j.socscimed.2010.10.023. PMid:21168249.

Dufour, É., Laporte, G., Paquette, J., & Rancourt, M.-È. (2018). Logistics service network design for humanitarian response in East Africa. Omega, 74, 1-14. http://dx.doi.org/10.1016/j.omega.2017.01.002.

Duran, S., Ergun, O., Keskinocak, P., & Swann, J. (2013) Humanitarian logistics: advanced purchasing and pre-positioning of relief items. In J. BOOKBINDER (Ed.), Handbook of Global Logistics, International Series in Operations Research and Management Science (pp. 447-462). New York: Springer. http://dx.doi.org/10.1007/978-1-4419-6132-7_18.

Falasca, M., & Zobel, C. W. (2011). A two-stage procurement model for humanitarian relief supply chains. Journal of Humanitarian Logistics and Supply Chain Management., 1(2), 151-169. http://dx.doi.org/10.1108/20426741111188329.

Ferrer, J. M., Martín-Campo, F. J., Ortuño, M. T., Pedraza-Martínez, A. J., Tirado, G., & Vitoriano, B. (2018). Multi-criteria optimization for last mile distribution of disaster relief aid: test cases and applications. European Journal of Operational Research, 269(2), 501-515. http://dx.doi.org/10.1016/j.ejor.2018.02.043.

Food and Agriculture Organization, International Fund for Agricultural Development, United Nations International Children's Emergency Fund, World Food Programme, World Health Organization. (2019). The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns. Rome: FAO.

Gentilini, U. (2013). Banking on Food: the state of food banks in high-income countries. IDS Working Papers, 2013(415), 1-18.

Gonçalves, P., Leiras, A., Chawaguta, B., & Yoshizaki, H. T. Y. (2013). Stochastic optimization of humanitarian aid supply and distribution for the WFP in Ethiopia. In K. Singhal (Ed.), 24th Annual POMS Conference - Production and Operations Management Society. Denver, Colorado: Integrating Practice in POM Research and Teaching.

Grass, E., & Fischer, K. (2016). Two-stage stochastic programming in disaster management: a literature survey. Surveys in Operations Research and Management Science, 21(2), 85-100. http://dx.doi.org/10.1016/j.sorms.2016.11.002.

Holguín-Veras, J., Amaya-Leal, J., Cantillo, V., Van Wassenhove, L. N., Aros-Vera, F., & Jaller, M. (2016). Econometric estimation of deprivation cost functions: a contingent valuation experiment. Journal of Operations Management, 45(1), 44-56. http://dx.doi.org/10.1016/j.jom.2016.05.008.

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. http://dx.doi.org/10.1016/j.jom.2013.06.002.

Hoyos, M. C., Morales, R. S., & Akhavan-Tabatabaei, R. (2015). OR models with stochastic components in disaster operations management: a literature survey. Computers & Industrial Engineering, 82, 183-197. http://dx.doi.org/10.1016/j.cie.2014.11.025.

Hwang, H.-S. (1999). A food distribution model for famine relief. Computers & Industrial Engineering, 37(1-2), 335-338. http://dx.doi.org/10.1016/S0360-8352(99)00087-X.

King, A. J., & Wallace, S. W. (2012). Modeling with Stochastic Programming (pp. 33-60). New York: Springer New York. http://dx.doi.org/10.1007/978-0-387-87817-1_2.

Kouwenberg, R. (2001). Scenario generation and stochastic programming models for asset liability management. European Journal of Operational Research, 134(2), 279-292. http://dx.doi.org/10.1016/S0377-2217(00)00261-7.

Lamenza, A. A. S., Fontainha, T. C., & Leiras, A. (2019). Purchasing strategies for relief items in humanitarian operations. Journal of Humanitarian Logistics and Supply Chain Management, 9(2), 151-171. http://dx.doi.org/10.1108/JHLSCM-09-2018-0060.

LaMont-Gregory, E., Henry, C. J. K., & Ryan, T. J. (1995). Evidence-based humanitarian relief interventions. Lancet, 346(8970), 312-313. http://dx.doi.org/10.1016/S0140-6736(95)92199-0. PMid:7630268.

Li, A. C. Y., Nozick, L., Xu, N., & Davidson, R. (2012). Shelter location and transportation planning under hurricane conditions. Transportation Research Part E, Logistics and Transportation Review, 48(4), 715-729. http://dx.doi.org/10.1016/j.tre.2011.12.004.

Lien, R. W., Iravani, S. M. R., & Smilowitz, K. R. (2014). Sequential resource allocation for nonprofit operations. Operations Research, 62, 301-317.

Long, D. C., & Wood, D. F. (1995). The logistics of famine relief. Journal of Business Logistics, 16(1), 213-230.

Maxwell, D., Young, H., Jaspars, S., Frize, J., & Burns, J. (2011). Targeting and distribution in complex emergencies: participatory management of humanitarian food assistance. Food Policy, 36(4), 535-543. http://dx.doi.org/10.1016/j.foodpol.2011.03.010.

Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126(1), 76-84. http://dx.doi.org/10.1016/j.ijpe.2009.10.004.

Murali, P., Ordóñez, F., & Dessouky, M. M. (2012). Facility location under demand uncertainty: response to a large-scale bio-terror attack. Socio-Economic Planning Sciences, 46(1), 78-87. http://dx.doi.org/10.1016/j.seps.2011.09.001.

Noyan, N., Balcik, B., & Atakan, S. (2015). A stochastic optimization model for designing last mile relief networks. Transportation Science, 50(3), 1092-1113. http://dx.doi.org/10.1287/trsc.2015.0621.

Orgut, I. S., Ivy, J., Uzsoy, R., & Wilson, J. R. (2016). Modeling for the equitable and effective distribution of donated food under capacity constraints. IIE Transactions, 48(3), 252-266.

Paul, J. A., & Wang, X. J. (2015). Robust optimization for United States Department of Agriculture food aid bid allocations. Transportation Research Part E, Logistics and Transportation Review, 82, 129-146. http://dx.doi.org/10.1016/j.tre.2015.08.001.

Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2013). Vehicle replacement in the International Committee of the Red Cross. Production and Operations Management, 22(2), 365-376. http://dx.doi.org/10.1111/j.1937-5956.2011.01316.x.

Pérez-Rodríguez, N., & Holguín-Veras, J. (2016). Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transportation Science, 50(4), 1261-1285. http://dx.doi.org/10.1287/trsc.2014.0565.

Peters, K., Silva, S., Gonçalves, R., Kavelj, M., Fleuren, H., Hertog, D., Ergun, O., & Freeman, M. (2021). The nutritious supply chain: optimizing humanitarian food assistance. INFORMS Journal on Optimization, 3(2), 200-226. http://dx.doi.org/10.1287/ijoo.2019.0047.

Rancourt, M. E., Cordeau, J. F., Laporte, G., & Watkins, B. (2015). Tactical network planning for food aid distribution in Kenya. Computers & Operations Research, 56, 68-83. http://dx.doi.org/10.1016/j.cor.2014.10.018.

Rath, S., Gendreau, M., & Gutjahr, W. J. (2016). Bi-objective stochastic programming models for determining depot locations in disaster relief operations. International Transactions in Operational Research, 23(6), 997-1023. http://dx.doi.org/10.1111/itor.12163.

Rawls, C. G., & Turnquist, M. A. (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic Planning Sciences, 46(1), 46-54. http://dx.doi.org/10.1016/j.seps.2011.10.002.

Ribas, G. P., Leiras, A., & Hamacher, S. (2012). Operational planning of oil refineries under uncertainty. IMA Journal of Management Mathematics, 23(4), 397-412. http://dx.doi.org/10.1093/imaman/dps005.

Rottkemper, B., Fischer, K., Blecken, A., & Danne, C. (2011). Inventory relocation for overlapping disaster settings in humanitarian operations. OR Spectrum, 3(3), 721–749.

Salmerón, J., & Apte, A. (2010). Stochastic optimization for natural disaster asset prepositioning. Production and Operations Management, 19(5), 561-574. http://dx.doi.org/10.1111/j.1937-5956.2009.01119.x.

Turner, R. (2013). Modelling barriers to customs entry at the time of disaster in developing countries —mitigating the delay of life-saving materials (Maters thesis). University of Lugano, Switzerland.

United Nations. (2018). The Sustainable Development Goals Report 2018. New York: United Nations. Retrieved in 2021, June 2, from https://unstats.un.org/sdgs/files/report/2018/TheSustainableDevelopmentGoalsReport2018-EN.pdf.

Whybark, D. C. (2007). Issues in managing disaster relief inventories. International Journal of Production Economics, 108(1-2), 228-235. http://dx.doi.org/10.1016/j.ijpe.2006.12.012.

World Customs Organization. (2015). Retrieved in 2017, September 14, from http://www.wcoomd.org/en/about-us/wco-members/membership.aspx.

World Food Programme (2016). WFP Ethiopia Country Brief December 2016. Retrieved in 2017, September 14, from http://documents.wfp.org/stellent/groups/public/documents/ep/wfp273887.pdf

World Food Programme. (2011). Facts Blast – Foods Purchase in 2010. May, 2011. Retrieved in 2017, September 14, from http://documents.wfp.org/stellent/groups/public/documents/communications/wfp187701.pdf

World Food Programme. (2015). Retrieved in 2017, September 14, from http://www.wfp.org.

Yigezu, A. Y., & Sanders, J. H. (2012). Introducing new agricultural technologies and marketing strategies: a means for increasing income and nutrition of farm households in Ethiopia. African Journal of Food, Agriculture, Nutrition and Development, 12(5), 6365-6384.
 


Submitted date:
06/02/2021

Accepted date:
09/25/2021

6149d42ba953951a93295434 production Articles
Links & Downloads

Production

Share this page
Page Sections