A Model for Prepositioning Emergency Relief Items Before a Typhoon with an Uncertain Trajectory

References

• Alem D, Clark A, Moreno A (2016) Stochastic network models for logistics planning in disaster relief. Eur. J. Oper. Res. 255(1):187–206.Crossref, Google Scholar
• Anaya-Arenas AM, Renaud J, Ruiz A (2014) Relief distribution networks: A systematic review. Ann. Oper. Res. 223(1):53–79.Crossref, Google Scholar
• Balcik B, Beamon BM (2008) Facility location in humanitarian relief. Internat. J. Logist. Res. Appl. 11(2):101–121.Crossref, Google Scholar
• Balcik B, Bozkir CDC, Kundakcioglu OE (2016) A literature review on inventory management in humanitarian supply chains. Surveys Oper. Res. Management Sci. 21(2):101–116.Crossref, Google Scholar
• Bozorgi-Amiri A, Jabalameli MS, Al-e Hashem SM (2013) A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum 35(4):905–933.Crossref, Google Scholar
• Campbell AM, Jones PC (2011) Prepositioning supplies in preparation for disasters. Eur. J. Oper. Res. 209(2):156–165.Crossref, Google Scholar
• Caunhye AM, Nie X, Pokharel S (2012) Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sci. 46(1):4–13.Crossref, Google Scholar
• Chang M-S, Tseng Y-L, Chen J-W (2007) A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Res. Part E Logist. Transportation Rev. 43(6):737–754.Crossref, Google Scholar
• Commission on Audit (2014) Report on the audit of Typhoon Yolanda relief operations. Technical report, Commission on Audit (COA), Republic of the Philippines.Google Scholar
• Davis LB, Samanlioglu F, Qu X, Root S (2013) Inventory planning and coordination in disaster relief efforts. Internat. J. Production Econom. 141(2):561–573.Crossref, Google Scholar
• Department of Social Welfare and Development, Republic of the Philippines (2015) Disaster Response Operations Guidelines. Accessed October 6, 2020, https://www.dswd.gov.ph/issuances/AOs/AO_2015-003.pdf.Google Scholar
• Department of Social Welfare and Development, Republic of the Philippines (2017) LGUs are the first responders to disaster response, DSWD to augment resources. Press Release. Accessed on October 6, 2020, https://www.dswd.gov.ph/lgus-are-the-first-responders-to-disaster-response-dswd-to-augment-resources/.Google Scholar
• Duran S, Gutierrez MA, Keskinocak PN (2011) Pre-positioning of emergency items worldwide for CARE International. Interfaces 41(3):223–237.Link, Google Scholar
• Federal Emergency Management Agency (2020) Hazus user & technical manuals. Accessed on November 20, 2020, https://www.fema.gov/flood-maps/tools-resources/flood-map-products/hazus/user-technical-manuals.Google Scholar
• Fritz A (2014) Landfall or not? Forecasts disagree on path of dangerous Typhoon Hagupit as it nears the Philippines. Washington Post Blog (December 3), http://www.washingtonpost.com/blogs/capital-weather-gang/wp/2014/12/03/landfall-or-not-forecasts-disagree-on-path-of-dangerous-typhoon-hagupit-as-it-nears-the-philippines/.Google Scholar
• Galindo G, Batta R (2013a) Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Economic Planning Sci. 47(1):20–37.Crossref, Google Scholar
• Galindo G, Batta R (2013b) Review of recent developments in OR/MS research in disaster operations management. Eur. J. Oper. Res. 230(2):201–211.Crossref, Google Scholar
• Gallien J, Graves SC, Scheller-Wolf A (2016) OM Forum–Practice-based research in operations management: What it is, why do it, related challenges, and how to overcome them. Manufacturing Service Oper. Management 18(1):5–14.Link, Google Scholar
• Gralla E, Goentzel J, Fine C (2014) Assessing trade-offs among multiple objectives for humanitarian aid delivery using expert preferences. Production Oper. Management 23(6):978–989.Crossref, Google Scholar
• Gutjahr WJ, Pichler A (2016) Stochastic multi-objective optimization: A survey on non-scalarizing methods. Ann. Oper. Res. 236(2016):475–499.Crossref, Google Scholar
• Holguin-Veras J, Perez N, Jaller M, Wassenhove LNV, Arros-Vera F (2013) On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Management 31(5):262–280.Crossref, Google Scholar
• Holland GJ (1980) An analytic model of the wind and pressure profiles in hurricanes. Monthly Weather Rev. 108(8):1212–1218.Crossref, Google Scholar
• Hong X, Lejeune MA, Noyan N (2015) Stochastic network design for disaster preparedness. IIE Trans. 47(4):329–357.Crossref, Google Scholar
• Hoyos MC, Morales RS, Akhavan-Tabatabaei R (2015) OR models with stochastic components in disaster operations management: A literature survey. Comput. Indust. Engrg. 82(2015):183–197.Crossref, Google Scholar
• International Rice Research Institute (2013). Satellite images map of rice damage from typhoon (press release). Accessed May 16, 2015, http://irri.org/news/media-releases/satellite-images-map-rice-damage-from-typhoon.Google Scholar
• Kovacs G, Moshtari M (2019) A roadmap for higher research quality in humanitarian operations: A methodological perspective. Eur. J. Oper. Res. 276(2):395–408.Crossref, Google Scholar
• Lodree EJ, Taskin S (2008) An insurance risk management framework for disaster relief and supply chain disruption inventory planning. J. Oper. Res. Soc. 59(5):674–684.Crossref, Google Scholar
• Lodree EJ, Taskin S (2009) Supply chain planning for hurricane response with wind speed information updates. Comput. Oper. Res. 36(1):2–15.Crossref, Google Scholar
• Lodree EJ, Ballard KN, Song CH (2012) Pre-positioning hurricane supplies in a commercial supply chain. Socio-Economic Planning Sci. 46(4):291–305.Crossref, Google Scholar
• Mete HO, Zabinsky ZB (2010) Stochastic optimization of medical supply location and distribution in disaster management. Internat. J. Production Econom. 126(1):76–84.Crossref, Google Scholar
• National Disaster Risk Reduction and Management Council (2014a) National Disaster Response Plan (NDRP). Accessed October 6, 2020, http://www.ndrrmc.gov.ph/attachments/article/1334/NDRP_Hydro_Meteorological_Hazards_as_of_2014.pdf.Google Scholar
• National Disaster Risk Reduction and Management Council (2014b) Y it happened: Learning from Typhoon Yolanda. Technical report, National Disaster Risk Reduction and Management Council (NDRRMC), Republic of the Philippines.Google Scholar
• Noyan N, Balcik B, Atakan S (2016) A stochastic optimization model for designing last mile relief networks. Transportation Sci. 50(3):1092–1113.Link, Google Scholar
• Official Gazette (2014) FAQs: Government’s immediate response to Typhoon Yolanda. Official Gazette. Accessed May 20, 2016, https://web.archive.org/web/20161011174512/https://www.gov.ph/2014/04/24/faqs-governments-immediate-response-to-typhoon-yolanda/.Google Scholar
• Pacheco GG, Batta R (2016) Forecast-driven model for prepositioning supplies in preparation for a foreseen hurricane. J. Oper. Res. Soc. 67(1):98–113.Crossref, Google Scholar
• Philippine Atmospheric Geophysical and Astronomical Services Administration (2009) Member Report to the ESCAP/WMO Typhoon Committee 41st Session. Technical report, Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Quezon City, Philippines.Google Scholar
• PTV News Philippines (2014) DSWD cites lessons learned from typhoon ‘Yolanda’ in preparing for future calamities. Accessed May 13, 2016, https://web.archive.org/web/20160525140337/http://ptvnews.ph/features-bottom/32308-dswd-cites-lessons-learned-from-typhoon-yolanda-in-preparing-for-future-calamities.Google Scholar
• Rath S, Gendreau M, Gutjahr WJ (2016) Bi-objective stochastic programming models for determining depot locations in disaster relief operations. Internat. Trans. Oper. Res. 23(6):997–1023.Crossref, Google Scholar
• Raudenbush SW, Bryk AS (2002) Hierarchical Linear Models: Applications and Data Analysis Methods, Advances Quantitative Techniques in the Social Sciences Series, vol. 1 (Sage, Thousand Oaks, CA).Google Scholar
• Rawls CG, Turnquist MA (2011) Pre-positioning planning for emergency response. OR Spectrum 33(2011):481–498.Crossref, Google Scholar
• Sabillo KA (2013) Relief to reach 100% of Yolanda-battered Leyte Thursday – Palace. Inquirer.net (November 14), http://newsinfo.inquirer.net/527273/relief-to-reach-100-of-yolanda-battered-leyte-thursday-palace.Google Scholar
• Salas LC, Cárdenas MR, Zhang M (2012) Inventory policies for humanitarian aid during hurricanes. Socio-Economic Planning Sci. 46(4):272–280.Crossref, Google Scholar
• Taskin S, Lodree EJ (2010) Inventory decisions for emergency supplies based on hurricane count predictions. Internat. J. Production Econom. 126(1):66–75.Crossref, Google Scholar
• Taskin S, Lodree E Jr (2011) A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management. J. Oper. Res. Soc. 62(6):1098–1108.Crossref, Google Scholar
• Tofighi S, Torabi SA, Mansouri SA (2016) Humanitarian logistics network design under mixed uncertainty. Eur. J. Oper. Res. 250(1):239–250.Crossref, Google Scholar
• United Nations Economic and Social Commission for Asia and the Pacific (2013) Statistical Yearbook for Asia and the Pacific 2013 (United Nations Publications, New York).Google Scholar
• United Nations World Food Programme (2016) Revamped response centre ready to product 50,000 emergency family food packs (news release). Accessed October 10, 2020, https://www.wfp.org/news/dswd-wfp-launch-disaster-response-centre-visayas.Google Scholar
• World Risk Report (2020) Bündnis Entwicklung Hilft. Accessed May 7, 2021, http://www.worldriskreport.org/.Google Scholar
• Ye Y, Jiao W, Yan H (2020) Managing relief inventories responding to natural disasters: Gaps between practice and literature. Production Oper. Management 29(4):807–832.Crossref, Google Scholar