Production
http://www.production.periodikos.com.br/article/doi/10.1590/0103-6513.20200042
Production
Research Article

A multi-criteria stochastic programming approach for pre-positioning disaster relief supplies in Brazil

Irineu de Brito Junior; Adriana Leiras; Hugo Tsugunobu Yoshida Yoshizaki

Downloads: 0
Views: 67

Abstract

Abstract: Paper aims: Considering that disaster preparedness is essential for a prompt and effective response, this paper presents a study to locate disaster relief supplies.

Originality: This paper marks the first time a multi-criteria stochastic methodology addresses humanitarian location problems.

Research method: We propose a multi methodology approach that employs an optimization model and a multi-criteria decision analysis. Based on logistics costs and penalties assigned for unmet demand, a stochastic model minimizes the total operational cost of opening distribution centers for pre-positioning disaster relief supplies. As decisions in humanitarian operations have multiple criteria and small differences in costs may not be significant by considering other criteria, we perform an analysis of the stochastic model solutions through Multi-criteria Decision Analysis.

Main findings: The findings show that the stochastics model leads to good results in uncertainty accommodation and that the consideration of qualitative and quantitative criteria improves decisions in humanitarian operations, especially when the supplies available are not enough to meet all the demand requirements.

Implications for theory and practice: The methodology was used by Civil Defense to locate warehouses for prepositioning relief supplies in Sao Paulo State, Brazil.

Keywords

Humanitarian logistics, Facility location, Stochastic optimization, MCDA. Multimethodology

References

Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics: Research and Applications, 11(2), 101-121. http://dx.doi.org/10.1080/13675560701561789.

Balcik, B., Bozkir, C. D. C., & Kundakcioglu, O. E. (2016). A literature review on inventory management in humanitarian supply chains. Surveys in Operations Research and Management Science, 21(2), 101-116. http://dx.doi.org/10.1016/j.sorms.2016.10.002.

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.

Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: an integrated approach (1st ed.). Norwell, Massachusetts: Kluwer Academic Publishers. http://dx.doi.org/10.1007/978-1-4615-1495-4.

Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming (2nd ed., Series in Operations Research and Financial Engineering). New York: Springer. http://dx.doi.org/10.1007/978-1-4614-0237-4

Blecken, A., Hellingrath, B., Dangelmaier, W., & Schulz, S. F. (2009). A humanitarian supply chain process reference model. International Journal of Services Technology and Management, 12(4), 391. http://dx.doi.org/10.1504/IJSTM.2009.025815.

Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498. http://dx.doi.org/10.1016/j.ijdrr.2017.01.017.

Bozorgi-Amiri, A., Jabalameli, M. S., & Mirzapour Al-e-Hashem, S. M. J. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR-Spektrum, 35(4), 905-933. http://dx.doi.org/10.1007/s00291-0B11-0268-x.

Brasil. (2007). Política Nacional de Defesa Civil. Brasília: Ministério da Integração Nacional, Secretaria Nacional de Defesa Civil.

Brito Junior, I., Leiras, A., & Yoshizaki, H. T. Y. (2014, May 9-12). Pre-positioning relief supplies in Brazil through location decisions. In. Proceedings of POMS 25th Annual Conference. Atlanta, USA: POMS - Production and Operations Management Society. http://www.pomsmeetings.org/EventsNet/evNet/evNetSessBrowse/BrowseAbs.aspx?pr=1&ev=51

Bruno, G., & Genovese, A. (2016). Location analysis for public sector decision-making in uncertain times: an introduction to the special issue. Socio-Economic Planning Sciences, 53, 2-3. http://dx.doi.org/10.1016/j.seps.2016.02.002.

Budnitz, R. J., Apostolakis, G., Boore, D. M., Cluff, L. S., Coppersmith, K. J., Cornell, C. A., & Morris, P. A. (1998). Use of technical expert panels: applications to probabilistic seismic hazard analysis. Risk Analysis, 18(4), 463-469. http://dx.doi.org/10.1111/j.1539-6924.1998.tb00361.x.

Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: a literature review. Socio-Economic Planning Sciences, 46(1), 4-13. http://dx.doi.org/10.1016/j.seps.2011.04.004.

Carland, C., Goentzel, J., & Montibeller, G. (2018). Modeling the values of private sector agents in multi-echelon humanitarian supply chains. European Journal of Operational Research, 269(2), 532-543. http://dx.doi.org/10.1016/J.EJOR.2018.02.010.

Chang, M.-S., Tseng, Y.-L., & Chen, J.-W. (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E, Logistics and Transportation Review, 43(6), 737-754. http://dx.doi.org/10.1016/j.tre.2006.10.013.

Cheng, S., Chan, C. W., & Huang, G. H. (2003). An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management. Engineering Applications of Artificial Intelligence, 16(5-6), 543-554. http://dx.doi.org/10.1016/S0952-1976(03)00069-1.

Chyi, H. I., & McCombs, M. (2004). Media salience and the process of framing: coverage of the Columbine school shootings. Journalism & Mass Communication Quarterly, 81(1), 22-35. http://dx.doi.org/10.1177/107769900408100103.

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

Cotes, N., & Cantillo, V. (2019). Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89-100. http://dx.doi.org/10.1016/j.seps.2018.03.002.

Dantzig, G. B. (1955). Linear programming under uncertainty. Management Science, 1(3-4), 197-206. http://dx.doi.org/10.1287/mnsc.1.3-4.197.

Franco, L. A., & Montibeller, G. (2011). Problem structuring for multicriteria decision analysis interventions. In J. J. Cochran (Ed.), Wiley Encyclopedia of operations research and management science. Hoboken: John Wiley & Sons. http://dx.doi.org/10.1002/9780470400531.eorms0683

García Valdés, M., & Suárez Marín, M. (2013). El método Delphi para la consulta a expertos en la investigación científica. Revista Cubana de Salud Pública, 39(2), 253-267. Retrieved October 20, 2016, from http://bvs.sld.cu/revistas/spu/vol39_2_13/spu07213.htm

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.

Gutjahr, W. J., & Fischer, S. (2018). Equity and deprivation costs in humanitarian logistics. European Journal of Operational Research, 270(1), 185-197. http://dx.doi.org/10.1016/j.ejor.2018.03.019.

Gutjahr, W. J., & Nolz, P. C. (2016). Multicriteria optimization in humanitarian aid. European Journal of Operational Research, 252(2), 351-366. http://dx.doi.org/10.1016/j.ejor.2015.12.035.

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.

Houston, J. B., Pfefferbaum, B., & Rosenholtz, C. E. (2012). Disaster news: framing and frame changing in coverage of major U.S. natural disasters, 2000-2010. Journalism & Mass Communication Quarterly, 89(4), 606-623. http://dx.doi.org/10.1177/1077699012456022.

Instituto Brasileiro de Geografia e Estatística – IBGE. (2016). Síntese de indicadores 2015. Retrieved October 20, 2016, from http://www.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/pnad2015/sintese_defaultxls.shtm

Jeworrek, T. (2017). Natural disasters in 2017 were a sign of things to come: new coverage concepts are needed. Munich: Munich RE. Retrieved October 20, 2016, from https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/natural-disasters-2017.html

Keeney, R. L. (1992). Value-focused thinking: a path to creative decision-making. Cambridge: Harvard University Press.

Kessler, M. (2013). Logistics network design in Africa. Berne: Haupt Verlag.

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

Kunz, N., & Reiner, G. (2012). A meta-analysis of Humanitarian Logistics research. Journal of Humanitarian Logistics and Supply Chain Management, 2(2), 116-147. http://dx.doi.org/10.1108/20426741211260723.

Leiras, A., Brito Junior, I., Peres, E. Q., Bertazzo, T. R., & Yoshizaki, H. T. Y. (2014). Literature review of humanitarian logistics research: trends and challenges. Journal of Humanitarian Logistics and Supply Chain Management, 4(1), 95-130. http://dx.doi.org/10.1108/JHLSCM-04-2012-0008.

Leiras, A., Ribas, G., Hamacher, S., & Elkamel, A. (2013). Tactical and operational planning of multirefinery networks under uncertainty: an iterative integration approach. Industrial & Engineering Chemistry Research, 52(25), 8507-8517. http://dx.doi.org/10.1021/ie302835n.

Mendoza, G. A., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. Forest Ecology and Management, 230(1-3), 1-22. http://dx.doi.org/10.1016/j.foreco.2006.03.023.

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.

Montibeller, G., & Franco, L. A. (2007). Decision and risk analysis for the evaluation of strategic options. In F. A. O’Brien & R. G. Dyson (Eds.), Supporting strategy: frameworks, methods and models (pp. 251-284). Chichester: Wiley.

Montibeller, G., Gummer, H., & Tumidei, D. (2006). Combining scenario planning and multi-criteria decision analysis in practice. Journal of Multi-Criteria Decision Analysis, 14(1-3), 5-20. http://dx.doi.org/10.1002/mcda.403.

Nolz, P. C., Doerner, K. F., & Hartl, R. F. (2010). Water distribution in disaster relief. International Journal of Physical Distribution & Logistics Management, 40(8/9), 693-708. http://dx.doi.org/10.1108/09600031011079337.

Noyan, N. (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research, 39(3), 541-559. http://dx.doi.org/10.1016/j.cor.2011.03.017.

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.

Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological, 44(4), 521-534. http://dx.doi.org/10.1016/j.trb.2009.08.003.

Rawls, C. G., & Turnquist, M. A. (2011). Pre-positioning planning for emergency response with service quality constraints. OR-Spektrum, 33(3), 481-498. http://dx.doi.org/10.1007/s00291-011-0248-1.

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.

Rowe, G., & Wright, G. (2001). Expert opinions in forecasting: the role of the Delphi technique. In J. S. Armstrong (Ed.), Principles of forecasting: a handbook for researchers and practitioners (pp. 125-144). Dordrecht: Kluwer Academic Publishers. http://dx.doi.org/10.1007/978-0-306-47630-3_7.

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.

Sen, S., & Higle, J. L. (1999). An introductory tutorial on stochastic linear programming models. Interfaces, 29(2), 33-61. http://dx.doi.org/10.1287/inte.29.2.33.

Shapiro, A., Dentcheva, D., & Ruszczynski, A. (2009). Lectures on stochastic programming: modeling and theory. Philadelphia: SIAM. http://dx.doi.org/10.1137/1.9780898718751.

The Sphere Project (2011). The Sphere Project humanitarian charter and minimum standards in humanitarian response (3rd ed., Vol. 1). Rugby, UK: Practical Action Publishing. Retrieved October 20, 2016, from www.practicalactionpublishing.org/sphere

Tomasini, R., & Van Wassenhove, L. N. (2009). Humanitarian logistics (1st ed.). London: Palgrave Macmillan. http://dx.doi.org/10.1057/9780230233485.

Turkeš, R., & Sörensen, K. (2019). Instances for the problem of pre-positioning emergency supplies. Journal of Humanitarian Logistics and Supply Chain Management, 9(2), 172-195. https://doi.org/10.1108/JHLSCM-02-2018-0016.

United Nations High Commissioner for Refugees – UNHCR. (2007). Handbook for emergencies (3rd ed.). Geneva: UNHCR.

Verma, A., & Gaukler, G. M. (2015). Pre-positioning disaster response facilities at safe locations: an evaluation of deterministic and stochastic modeling approaches. Computers & Operations Research, 62, 197-209. http://dx.doi.org/10.1016/j.cor.2014.10.006.

Vitoriano, B., Ortuño, M. T., Tirado, G., & Montero, J. (2011). A multi-criteria optimization model for humanitarian aid distribution. Journal of Global Optimization, 51(2), 189-208. http://dx.doi.org/10.1007/s10898-010-9603-z.

Wallemacq, P., & House, R. (2018). Economic losses, poverty & disasters 1998-2017. Geneva: UNISDR. Retrieved October 20, 2016, from https://www.unisdr.org/files/61119_credeconomiclosses.pdf

Wang, X., Wang, X., Liang, L., Yue, X., & Van Wassenhove, L. N. (2017). Estimation of deprivation level functions using a numerical rating scale. Production and Operations Management, 26(11), 2137-2150. http://dx.doi.org/10.1111/poms.12760.

Zagefka, H., Noor, M., Brown, R., de Moura, G. R., & Hopthrow, T. (2011). Donating to disaster victims : Responses to natural and humanly caused events. European Journal of Social Psychology, 41(3), 353-363. http://dx.doi.org/10.1002/ejsp.781.
 


Submitted date:
04/27/2020

Accepted date:
06/15/2020

5f2c466e0e8825293fbd1625 production Articles
Links & Downloads

Production

Share this page
Page Sections