References
1.Gökçe, M.A. and Ercan, E. “Multi-period vehiclerouting and replenishment problem of neighborhooddisaster stations for pre-disaster humanitarian relieflogistics”, IFAC-Papers Online, 52(13), pp. 2614-2619(2019). doi.org/10.1016/j.ifacol.2019.11.601.
2.Cao, C., Li, C., Yang, Q., et al. “A novel multi-objectiveprogramming model of relief distribution for sustainabledisaster supply chain in large-scale natural disasters”,Journal of Cleaner Production, 174, pp. 1422-1435 (2018).doi.org/10.1016/j.jclepro.2017.11.037.
3.Cao, C., Liu, Y., Tang, O., et al. “A fuzzy bi-leveloptimization model for multi-period post-disaster reliefdistribution in sustainable humanitarian supply chains”,International Journal of Production Economics, 235,108081 (2021). doi.org/10.1016/j.ijpe.2021.108081.
4.Boostani, A., Jolai, F., and Bozorgi-Amiri, A. “Designinga sustainable humanitarian relief logistics model in pre-andpostdisaster management”, International Journal ofSustainable Transportation, 15, pp. 1-17 (2020).doi.org/10.1080/15568318.2020.1773975.
5.Loree, N. and Aros-Vera, F. “Points of distributionlocation and inventory management model for post-disaster humanitarian logistics”, TransportationResearch Part E: Logistics and Transportation Review,116, pp. 1-24 (2018). doi.org/10.1016/j.tre.2018.05.003.
6.Rahmani, D., Zandi, A., Peyghaleh, E., et al. “A robustmodel for a humanitarian relief network with backupcovering under disruptions: A real world application”,International Journal of Disaster Risk Reduction, 28, pp. 56-68 (2018). doi.org/10.1016/j.ijdrr.2018.02.021.
7.Theeraviriya, C., Pitakaso, R., Sillapasa, K., et al. “Location decision making and transportation route planningconsidering fuel consumption”, Journal of OpenInnovation: Technology, Market, and Complexity, 5(2), p.27 (2019). doi.org/10.3390/joitmc5020027.
8.Shen, L., Tao, F., Shi, Y., et al. “Optimization oflocation-routing problem in emergency logisticsconsidering carbon emissions”, International Journal ofEnvironmental Research and Public Health, 16(16),2982 (2019). doi.org/10.3390/ijerph16162982.
9.Bozorgi-Amiri, A., Jabalameli, M.S., and Al-e-Hashem,S.M. “A multi-objective robust stochastic programmingmodel for disaster relief logistics under uncertainty”, ORSpectrum, 35(4), pp. 905-933 (2013). DOI:10.1007/s00291-011-0268-x.
10.Hatefi, S.M. and Jolai, F. “Robust and reliable forward–reverse logistics network design under demanduncertainty and facility disruptions”, AppliedMathematical Modeling, 38(9-10), pp. 2630-2647(2014). doi.org/10.1016/j.apm.2013.11.002.
11.An, S., Cui, N., Bai, Y., et al. “Reliable emergencyservice facility location under facility disruption, en-route congestion and in-facility queuing”,Transportation Research Part E: Logistics andTransportation Review, 82, pp. 199-216 (2015).doi.org/10.1016/j.tre.2015.07.006.
12.Huang, X. and Song, L. “An emergency logisticsdistribution routing model for unexpected events”,Annals of Operations Research, 269(1), pp. 223-239(2018). DOI:10.1007/s10479-016-2300-7.
13.Hu, C.L., Liu, X., and Hua, Y.K. “A bi-objective robustmodel for emergency resource allocation underuncertainty”, International Journal of ProductionResearch, 54(24), pp. 7421-7438 (2016). doi.org/10.1080/00207543.2016.1191692.
14.Haghi, M., Ghomi, S.M.T.F., and Jolai, F. “Developinga robust multi-objective model for pre/post disastertimes under uncertainty in demand and resource”,Journal of Cleaner Production, 154, pp. 188-202(2017). doi.org/10.1016/j.jclepro.2017.03.102.
15.Liu, Y., Lei, H., Wu, Z., et al. “A robust model predictivecontrol approach for post-disaster relief distribution”,Computers and Industrial Engineering, 135, pp. 1253-1270 (2019). doi.org/10.1016/j.cie.2018.09.005.
16.Lin, Y.H., Batta, R., Rogerson, P.A., et al. “A logisticsmodel for emergency supply of critical items in theaftermath of a disaster”, Socio-Economic PlanningSciences, 45(4), pp. 132-145 (2011).doi.org/10.1016/j.seps.2011.04.003.
17.Wang, H., Du, L., and Ma, S. “Multi-objective openlocation-routing model with split delivery for optimizedrelief distribution in post-earthquake”, TransportationResearch Part E: Logistics and Transportation Review,69, pp. 160-179 (2014).doi.org/10.1016/j.tre.2014.06.006.
18.Balcik, B. and Yanıkoğlu, İ. “A robust optimizationapproach for humanitarian needs assessment planningunder travel time uncertainty”, European Journal ofOperational Research, 282(1), pp. 40-57 (2020).doi.org/10.1016/j.ejor.2019.09.008.
19.Aghajani, M., Torabi, S.A., and Heydari, J. “A noveloption contract integrated with supplier selection andinventory prepositioning for humanitarian relief supplychains”, Socio-Economic Planning Sciences, 71, 100780 (2020). doi.org/10.1016/j.seps.2019.100780.
20.Habib, M.S., Lee, Y.H., and Memon, M.S.“Mathematical models in humanitarian supply chainmanagement: A systematic literature review”,Mathematical Problems in Engineering, 2016(1),Article ID 3212095, 20 pages (2016). DOI:10.1155/2016/3212095.
21.Haavisto, I. and Kovács, G. “Perspectives onsustainability in humanitarian supply chains”, DisasterPrevention and Management, 23(5), pp. 610-631(2014). DOI:10.1108/DPM-10-2013-0192.
22.Carter, C.R. and Rogers, D.S. “A framework ofsustainable supply chain management: moving towardnew theory”, International Journal of PhysicalDistribution and Logistics Management, 38, pp. 360-387 (2008). doi:10.1108/09600030810882816.
23.Kaivo-oja, J., Panula-Ontto, J., Vehmas, J., et al.“Relationships of the dimensions of sustainability asmeasured by the sustainable society index framework”,International Journal of Sustainable Development andWorld Ecology, 21(1), pp. 39-45 (2014).doi.org/10.1080/13504509.2013.860056.
24.Weerawardena, J., McDonald, R.E., and Mort, G.S.“Sustainability of nonprofit organizations: An empirical investigation”, Journal of World Business, 45(4), pp. 346-356 (2010). doi.org/10.1016/j.jwb.2009.08.004.
25.Ibegbunam, I. and McGill, D. “Health commoditiesmanagement system: priorities and challenges”, Journalof Humanitarian Logistics and Supply ChainManagement, 2(2), pp. 161-182 (2012). DOI:10.1108/20426741211260741.
26.Haavisto, I. and Kovacs, G. “Sustainability inhumanitarian operations”, Sustainable Value ChainManagement Analyzing, Designing, Implementing, andMonitoring for Social and EnvironmentalResponsibility, pp. 489-507 (2013).
27.Kunz, N. and Gold, S. “Sustainable humanitarian supplychain management-exploring new theory”, InternationalJournal of Logistics Research and Applications, 20(2),pp. 85-104 (2017). DOI:10.1080/13675567.2015.1103845.
28.Dubey, R. and Gunasekaran, A. “The sustainablehumanitarian supply chain design: agility, adaptabilityand alignment”, International Journal of LogisticsResearch and Applications, 19(1), pp. 62-82 (2016).doi.org/10.1080/13675567.2015.1015511.
29.Zhang, P., Liu, Y., Yang, G., et al. “A multi-objectivedistributionally robust model for sustainable last milerelief network design problem”, Annals of OperationsResearch, pp. 1-42 (2020). DOI:10.1007/s10479-020-03813-3.
30.Jamali, A., Ranjbar, A., Heydari, J., et al. “A multi-objective stochastic programming model to configure asustainable humanitarian logistics consideringdeprivation cost and patient severity”, Annals ofOperations Research, pp.1-36 (2021). DOI:10.1007/s10479-021-04014-2.
31.Haghani, S. C. O. A. “Testing and evaluation of a multi‐commodity multi‐modal network flow model fordisaster relief management”, Journal of AdvancedTransportation, 31(3), pp. 249-282 (1997).doi.org/10.1002/atr.5670310304.
32.Fiedrich, F., Gehbauer, F., and Rickers, U. “Optimizedresource allocation for emergency response afterearthquake disasters”, Safety Science, 35(1-3), pp. 41-57(2000). doi.org/10.1016/S0925-7535(00)00021-7.
33.Özdamar, L., Ekinci, E., and Küçükyazici, B.“Emergency logistics planning in natural disasters”,Annals of Operations Research, 129(1-4), pp. 217-245(2004). DOI:10.1023/B:ANOR.0000030690.27939.39.
34.Yi, W. and Kumar, A. “Ant colony optimization for disaster relief operations”, Transportation Research Part E: Logistics and Transportation Review, 43(6), pp. 660-672 (2007). doi.org/10.1016/j.tre.2006.05.004.
35.Mete, H.O. and Zabinsky, Z.B. “Stochastic optimizationof medical supply location and distribution in disastermanagement”, International Journal of ProductionEconomics, 126(1), pp. 76-84 (2010).doi.org/10.1016/j.ijpe.2009.10.004.
36.Najafi, M., Eshghi, K., and Dullaert, W. “A multi-objective robust optimization model for logisticsplanning in the earthquake response phase”,Transportation Research Part E: Logistics andTransportation Review, 49(1), pp. 217-249 (2013).doi.org/10.1016/j.tre.2012.09.001.
37.Veysmoradi, D., Vahdani, B., Farhadi Sartangi, M., etal. “Multi-objective open location-routing model forrelief distribution networks with split delivery and multi-mode transportation under uncertainty”, ScientiaIranica, 25(6), pp. 3635-3653 (2018). DOI:10.24200/sci.2017.4572.
38.Paydar, M.M., Molladavoodi, H., and Safaei, A.S.“Distribution planning of relief commoditiesconsidering the features of demand areas: A robustmulti-objective approach”, Scientia Iranica, 25(6), pp.3776-3793 (2018). DOI:10.24200/sci.2017.20009.
39.Shahparvari, S. and Bodaghi, B. “Risk reduction fordistribution of the perishable rescue items; Apossibilistic programming approach”, InternationalJournal of Disaster Risk Reduction, 31, pp. 886-901(2018). doi.org/10.1016/j.ijdrr.2018.07.018.
40.Cantillo, V., Serrano, I., Macea, L.F., et al. “Discretechoice approach for assessing deprivation cost inhumanitarian relief operations”, Socio-EconomicPlanning Sciences, 63, pp. 33-46 (2018).doi.org/10.1016/j.seps.2017.06.004.
41.Cotes, N. and Cantillo, V. “Including deprivation costsin facility location models for humanitarian relieflogistics”, Socio-Economic Planning Sciences, 65, pp.89-100 (2019). doi.org/10.1016/j.seps.2018.03.002.
42.Paul, J.A. and Wang, X.J. “Robust location-allocationnetwork design for earthquake preparedness”,Transportation Research Part B: Methodological, 119,pp. 139-155 (2019). doi.org/10.1016/j.trb.2018.11.009.
43.Eshghi, A., Tavakkoli-Moghaddam, R., Ebrahimnejad,S., et al. “Multi-objective robust mathematical modelingfor emergency relief in disaster under uncertainty”,Scientia Iranica, 29(5), pp. 2670-2695 (2020). DOI: 10.24200/sci.2020.54485.3770.
44.Madani, H., Arshadi Khamseh, A., and Tavakkoli-Moghaddam, R. “Solving a new bi-objective model forrelief logistics in a humanitarian supply chain by bi-objective meta-heuristic algorithms”, Scientia Iranica,28(5), pp. 2948-2971 (2021). DOI:10.24200/sci.2020.53823.3438.
45.Zhang, L. and Cui, N. “Pre-positioning facility location and resource allocation in humanitarian relief operationsconsidering deprivation costs”, Sustainability, 13(8), 4141(2021). doi.org/10.3390/su13084141.
46.Cheng, J., Feng, X., and Bai, X. “Modeling equitableand effective distribution problem in humanitarian relieflogistics by robust goal programming”, Computers andIndustrial Engineering, 155, 107183 (2021).doi.org/10.1016/j.cie.2021.107183.
47.Nayeem, M.K. and Lee, G.M. “Robust design of reliefdistribution networks considering uncertainty”,Sustainability, 13(16), 9281 (2021).doi.org/10.3390/su13169281.
48.Pishvaee, M.S., Rabbani, M., and Torabi, S.A. “A robust optimization approach to closed-loop supply chainnetwork design under uncertainty”, AppliedMathematical Modelling, 35(2), pp. 637-649 (2011).doi.org/10.1016/j.apm.2010.07.013.
49.Talaei, M., Moghaddam, B.F., Pishvaee, M.S., et al. “Arobust fuzzy optimization model for carbon-efficientclosed-loop supply chain network design problem: anumerical illustration in electronics industry”, Journalof Cleaner Production, 113, pp. 662-673 (2016). DOI:10.1016/j.jclepro.2015.10.074.
50.Inuiguchi, M. and Ramık, J. “Possibilistic linearprogramming: a brief review of fuzzy mathematicalprogramming and a comparison with stochasticprogramming in portfolio selection problem”, Fuzzy Setsand Systems, 111(1), pp. 3-28 (2000).doi.org/10.1016/S0165-0114(98)00449-7.
51.Charnes, A., Cooper, W.W., and Ferguson, R.O.“Optimal estimation of executive compensation bylinear programming”, Management Science, 1(2), pp.138-151 (1955). doi.org/10.1287/mnsc.1.2.138.
52.Podinovski, V.V. “Set choice problems with incompleteinformation about the preferences of the decisionmaker”, European Journal of Operational Research,207(1), pp. 371-379 (2010).doi.org/10.1016/j.ejor.2010.04.010.
53.Chang, C.T. “Revised multi-choice goal programming”,Applied Mathematical Modelling, 32(12), pp. 2587-2595(2008). doi.org/10.1016/j.apm.2007.09.008.
54. Urı́a, M.V.R., Caballero, R., Ruiz, F., et al. “Meta-goalprogramming”, European Journal of OperationalResearch, 136(2), pp. 422-429 (2002). DOI:10.1016/S0377-2217(00)00332-5.
55.Charnes, A. and Cooper, W.W. “Management modelsand industrial applications of linear programming”,Management Science, 4(1), pp. 38-91(1957).doi.org/10.1287/mnsc.4.1.38.
56.Lee, S.M. “Goal programming for decision analysis”,Philadelphia: Auerbach Publishers, 6(14), pp. 252-260(1972).
57.Ignizio, J.P. “Introduction to linear goal programming”,Beverly Hills, CA: Sage. pp. 1-96 (1985).
58.Tamiz, M., Jones, D., and Romero, C. “Goalprogramming for decision making: An overview of thecurrent state-of-the-art”, European Journal ofOperational Research, 111(3), pp. 569-581 (1998).doi.org/10.1016/S0377-2217(97)00317-2.
59.Romero, C. “Extended lexicographic goal programming: aunifying approach”, Omega, 29(1), pp. 63-71 (2001).DOI:10.1016/S0305-0483(00)00026-8.
60.Chang, C.T. “Multi-choice goal programming”, Omega,35(4), pp. 389-396 (2007).doi.org/10.1016/j.omega.2005.07.009.
61.Chang, C.T. “Multi - choice goal programming with utility functions”, European Journal of Operational Research, 215(2), pp. 439-445 (2011). doi.org/10.1016/j.ejor.2011.06.041.
62.Mohammed, F., Selim, S. Z., Hassan, A., et al. “Multi-period planning of closed-loop supply chain with carbonpolicies under uncertainty”, Transportation Research PartD: Transport and Environment, 51, pp. 146-172 (2017).https://doi.org/10.1016/j.trd.2016.10.033.
63.Columbia, B. “2012 BC Best Practices Methodology forQuantifying Greenhouse Gas Emissions IncludingGuidance for Public Sector Organizations, LocalGovernments and Community Emissions”, Ministry ofEnvironment (2012).
64.Jun, P., Gillenwater, M., and Barbour, W. “CO2, CH4,and N2O emissions from transportation-water-borne-navigation [Background paper]”, Good practiceguidance and uncertainty management in nationalgreenhouse gas inventories, pp.71-92(2002).
65.Holguín-Veras, J., Jaller, M., Van Wassenhove, L.N., etal. “On the unique features of post-disaster humanitarianlogistics”, Journal of Operations Management, 30(7-8),pp. 494-506 (2012). doi.org/10.1016/j.jom.2012.08.003.
)