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Design of experiments in humanitarian logistics: facility decision making in disaster preparedness. (English) Zbl 07745357

Summary: Facility planning is one of the critical decisions faced by humanitarian managers. Some managerial implications have been offered in the literature, but these are commonly derived from simple sensitivity analyses on individual instance characteristics and/or using a single case study, and as such can be misleading as they ignore important interactions between many disaster properties. We carried out a large experimental study that analyses the influence of different factors and their interactions on the choice of facility configuration for inventory pre-positioning in preparation for emergencies. On the one hand, the outcomes of the study provide insights on the effect of the most important factor interactions on the facility decision-making. On the other hand, the findings also demonstrate that the simple analyses might provide guidelines that are not robust across different disasters, and as such promote better experimental designs in the field of humanitarian logistics.
{© 2022 The Authors. International Transactions in Operational Research © 2022 International Federation of Operational Research Societies}

MSC:

90-XX Operations research, mathematical programming
Full Text: DOI

References:

[1] Al Theeb, N., Murray, C., 2017. Vehicle routing and resource distribution in postdisaster humanitarian relief operations. International Transactions in Operational Research24, 6, 1253-1284. · Zbl 1386.90083
[2] Altay, N., Green, W.G., 2006. OR/MS research in disaster operations management. European Journal of Operational Research175, 1, 475-493. · Zbl 1137.90574
[3] Anaya‐Arenas, A.M., Renaud, J., Ruiz, A., 2014. Relief distribution networks: a systematic review. Annals of Operations Research223, 1, 53-79. · Zbl 1306.90021
[4] Balcik, B., Ak, D., 2014. Supplier selection for framework agreements in humanitarian relief. Production and Operations Management23, 6, 1028-1041.
[5] Balcik, B., Beamon, B.M., 2008. Facility location in humanitarian relief. International Journal of Logistics11, 2, 101-121.
[6] 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 Science21, 2, 101-116.
[7] Bemley, J.L., Davis, L.B., BrockIII, L.G., 2013. Pre‐positioning commodities to repair maritime navigational aids. Journal of Humanitarian Logistics and Supply Chain Management3, 1, 65-89.
[8] Caunhye, A.M., Nie, X., Pokharel, S., 2012. Optimization models in emergency logistics: a literature review. Socio‐Economic Planning Sciences46, 1, 4-13.
[9] Cotts, D.G., Roper, K.O., Payant, R.P., 2009. The facility management handbook. AMACOM.
[10] Criqui, M.H., 1991. On the use of standardized regression coefficients. Epidemiology2, 5, 393.
[11] Galindo, G., Batta, R., 2013. Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research230, 2, 201-211.
[12] Greenland, S., Maclure, M., Schlesselman, J.J., Poole, C., Morgenstern, H., 1991. Standardized regression coefficients: a further critique and review of some alternatives. Epidemiology2, 5, 387-392.
[13] Greenland, S., Schlesselman, J.J., Criqui, M.H., 1986. The fallacy of employing standardized regression coefficients and correlations as measures of effect. American Journal of Epidemiology123, 2, 203-208.
[14] Habib, M.S., Lee, Y.H., Memon, M.S., 2016. Mathematical models in humanitarian supply chain management: a systematic literature review. Mathematical Problems in Engineering. https://doi.org/10.1155/2016/3212095 · doi:10.1155/2016/3212095
[15] Hoyos, M.C., Morales, R.S., Akhavan‐Tabatabaei, R., 2015. OR models with stochastic components in disaster operations management: a literature survey. Computers & Industrial Engineering82, 183-197.
[16] Luis, E., Dolinskaya, I.S., Smilowitz, K.R., 2012. Disaster relief routing: integrating research and practice. Socio‐Economic Planning Sciences46, 1, 88-97.
[17] Manopiniwes, W., Nagasawa, K., Irohara, T., 2014. Humanitarian relief logistics with time restriction: Thai flooding case study. Industrial Engineering & Management Systems13, 4, 398-407.
[18] McCoy, J., 2008. Humanitarian response: improving logistics to save lives. American Journal of Disaster Medicine3, 5, 283-293.
[19] Mejia‐Argueta, C., Gaytán, J., Caballero, R., Molina, J., Vitoriano, B., 2018. Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods. International Transactions in Operational Research25, 3, 1053-1079. · Zbl 1391.90562
[20] Ni, W., Shu, J., Song, M., 2018. Location and emergency inventory pre‐positioning for disaster response operations: Min‐max robust model and a case study of Yushu earthquake. Production and Operations Management27, 1, 160-183.
[21] Northwestern’s McCormick School of Engineering, 2010. Humanitarian logistics: Saving lives with better distribution models. https://www.mccormick.northwestern.edu/magazine/fall2010/humanitarianlogistics.html (accessed 5 September 2018).
[22] Noyan, N., 2012. Risk‐averse two‐stage stochastic programming with an application to disaster management. Computers & Operations Research39, 3, 541-559. · Zbl 1251.90251
[23] Özdamar, L., Ertem, M.A., 2015. Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research244, 1, 55-65. · Zbl 1346.90166
[24] Rawls, C.G., Turnquist, M.A., 2010. Pre‐positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological44, 4, 521-534.
[25] Sabbaghtorkan, M., Batta, R., He, Q., 2020. Prepositioning of assets and supplies in disaster operations management: review and research gap identification. European Journal of Operational Research284, 1, 1-19. · Zbl 1441.90037
[26] Tomasini, R.M., Van Wassenhove, L.N., 2009. From preparedness to partnerships: case study research on humanitarian logistics. International Transactions in Operational Research16, 5, 549-559. · Zbl 1187.90185
[27] Torabi, S.A., Shokr, I., Tofighi, S., Heydari, J., 2018. Integrated relief pre‐positioning and procurement planning in humanitarian supply chains. Transportation Research Part E: Logistics and Transportation Review113, 123-146.
[28] Turkeš, R., 2021. Design of experiments in humanitarian logistics: Facility decision making in disaster preparedness. Mendeley Data. https://doi.org/10.17632/b9fc88wp4x.3 · doi:10.17632/b9fc88wp4x.3
[29] Turkeš, R., Palhazi Cuervo, D., Sörensen, K., 2019. Pre‐positioning of emergency supplies: does putting a price on human life help to save lives?Annals of Operations Research283, 865-895.
[30] Turkeš, R., Sörensen, K., 2019. Instances for the problem of pre‐positioning emergency supplies. Journal of Humanitarian Logistics and Supply Chain Management9, 172-195.
[31] Turkeš, R., Sörensen, K., Cuervo, D.P., 2021. A matheuristic for the stochastic facility location problem. Journal of Heuristics.
[32] United Nations International Strategy for Disaster Reduction (UNISDR), 2009. Reducing disaster risks through science: issues and actions. the full report of the ISDR scientific and technical committee. http://www.unisdr.org/files/11543_STCReportlibrary.pdf (accessed 20 June 2018).
[33] Van Wassenhove, L.N., Pedraza Martinez, A.J., 2012. Using OR to adapt supply chain management best practices to humanitarian logistics. International Transactions in Operational Research19, 1‐2, 307-322.
[34] deVries, H., Van Wassenhove, L.N., 2017. Evidence‐based vehicle planning for humanitarian field operations. Working Paper No. 2017/62/TOM/Social Innovation Centre, INSEAD. https://ssrn.com/abstract=3039320.
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