Literature review of the case studies for the pre-positioning and related problems
| Article | Time | Location | Disaster type | Principal data sources | Main findings | Methodology |
|---|---|---|---|---|---|---|
| Balcik and Beamon (2008) | 1900–2006 | Worldwide | Earthquake | National Geophysical Data Center, Socioeconomic Data and Applications Center | Pre-disaster investments have a strong impact on the emergency strategy response time and proportion of demand satisfied | Sensitivity analysis on pre- and post-disaster budgets |
| Barzinpour and Esmaeili (2014) | – | Tehran | Earthquake | Risk Assessment tool for Diagnosis of Urban Areas against Seismic Disaster, GIS information | Virtual zoning approach (that helps the authorities create a better collaboration between neighboring local areas) yields lower logistics costs and greater coverage that municipal subregional zoning | Comparison of the solutions obtained by the two different approaches |
| Bemley et al. (2013) | 2005 | US Gulf Coast | Hurricane | US Coast Guard, National Weather Service, National Hurricane Center | Pre-positioning of repair supplies increases the ability of a port to quickly recover from disasters | Sensitivity analysis on the amount of available supply |
| Bozorgi-Amiri et al. (2013) | – | Tehran | Earthquake | Tehran Municipality Urban Planning and Research Center | It is beneficial to consider the total cost and people satisfaction simultaneously rather than individually; the same holds for different types of uncertainty | Comparison of the solutions obtained by the proposed bi-objective model and the two single-objective models; and models that consider some or all types of uncertainty |
| Chapman et al. (2014) | – | South Eastern US | Hurricane | Louisiana Homeland Security and Emergency Preparedness (Chapman, 2007) | Pre-positioning emergency supplies reduces both the logistics costs and the unmet demand | Comparison of the solutions obtained with or without a pre-positioning policy |
| de Brito Junior et al. (2013) | – | São Paulo State | – | Historical data and geographic information | Stochastic model generates more robust solutions | Comparison of the solutions obtained by a deterministic and stochastic model |
| Falasca and Zobel (2011) | – | – | – | Random instance | Stochastic programming on average reduces the logistics costs and the unmet demand compared to a simple expected value approach | Comparison of the solutions obtained by a deterministic and stochastic model |
| Galindo and Batta (2013) | 2005 | US Gulf Coast | Hurricane | National Hurricane Center, Internal Revenue Service e-File, Natural Resources Defense Council (Cornuéjols et al., 1991; Lin et al., 2011) | Pre-positioning reduces the logistics costs | Sensitivity analyses on the amplifying factor that indicates the number of times that the distribution costs are amplified after the disaster |
| Gonçalves et al. (2013) | 2009–2010 | Ethiopia | Food insecurity | World Food Program Ethiopia | The proposed model is a useful tool for food aid supply and distribution planning | Comparison of the solution obtained by the proposed model to the strategy carried out by World Food Program |
| Klibi et al. (2013) | 1964–2012 | North Carolina | – | North Carolina Emergency Management Division, Federal Emergency Management Agency | The proposed solution approach is useful for tackling humanitarian relief problems | Comparison of the solution obtained by the proposed solution approach with the existing network design |
| Li et al. (2011) | 1880–2007 | US Gulf Coast | Hurricane | North Atlantic Hurricane Database, State Government of Louisiana, American Red Cross (Klotzbach et al., 2014) | The proposed model and solution procedure can be used to efficiently solve the sheltering network planning | Demonstration of the solution algorithm convergence and efficiency for a real-world problem instance |
| Lodree et al. (2012) | – | US Gulf Coast | Hurricane | Rawls and Turnquist (2010) | Expected performance of the proposed pre-positioning strategy is more effective than the wait-and-see approach | Comparison of the solutions obtained using the two approaches |
| Manopiniwes et al. (2014) | 2011 | Thailand | Flood | Department of Disaster Prevention and Mitigation, Ministry of Interior of the Royal Thai Government | The proposed model can be used to optimize the facility locations and stocking decisions | Sensitivity analysis on different time and cost parameters |
| Mete and Zabinsky (2010) | – | Seattle | Earthquake | Cascadia Region Earthquake Workgroup, Earthquake Engineering Research Institute, Washington Military Department Emergency Management Division | The proposed model can aid interdisciplinary agencies to both prepare and respond to disasters | Application of the model to solve the case study |
| Moreno et al. (2016) | 2011 | Rio de Janeiro State | Flood | The Emergency Events Database, Instituto Brasileiro de Geografia e Estatística, International Federation of Red Cross and Red Crescent Societies (Altay and Green, 2006; Rawls and Turnquist, 2010) | The integration of decisions in a multi-period context and the option of reusing vehicles reduce total costs, thus improving the overall performance of the relief operations | Comparison of the solutions obtained by a single- and multi-period model, and with our without reusing the vehicles |
| Murali et al. (2012) | – | Los Angeles County | Anthrax attack | Bravata et al. (2006) | The proposed heuristic can be used to locate facilities to address large-scale emergencies | Comparison of the solutions obtained by the proposed locate-allocate heuristic and simulated annealing procedure introduced in (Berman and Drezner, 2006) |
| Noyan (2012) | – | US Gulf Coast | Hurricane | Rawls and Turnquist (2010) | The optimal location and allocation policies change with respect to the risk parameters that describe the level of conservativeness and risk-averseness | Sensitivity analysis on the risk parameters |
| Pradhananga et al. (2016) | – | US Gulf Coast | Hurricane | Rawls and Turnquist (2010) | The network and deprivation cost structure affects the preparedness and response planning decisions and resulting costs and level of service | Comparison of the solutions obtained by considering a single pre-selected potential supply point and the proposed three-echelon network structure with multiple supply points; and of the solutions obtained by minimizing constant, linearly- and the proposed exponentially-increasing deprivation costs |
| Rawls and Turnquist (2010) | – | US Gulf Coast | Hurricane | The Atlantic Oceanographic and Meteorological Laboratory | The proposed Lagrangian L-shaped heuristic can be used as an effective large-scale resource pre-positioning planning tool | Comparison of the solutions obtained by the proposed heuristic and CPLEX |
| Rawls and Turnquist (2011) | – | US Gulf Coast | Hurricane | Rawls and Turnquist (2010) | Adding the service quality constraints to the model results in more open facilities, reduced tendency of facilities to be specialized for storage of just one commodity and increased computational complexity | Comparison of the solutions obtained with and without service quality constraints |
| Renkli and Duran (2015) | – | Istanbul | Earthquake | Istanbul Metropolitan Municipality, Japan International Cooperation Agency | Adding a probabilistic constraint that ensures a certain service reliability opens storage facilities at safer but more distant locations from their assigned affected areas, thereby reducing the expected unsatisfied demand and increasing the average distance | Comparison of the solutions obtained by employing a model with and without reliability constraints |
| Rezaei-Malek and Tavakkoli-Moghaddam (2014) | – | Seattle | Earthquake | Mete and Zabinsky (2010) | It is beneficial to consider the response time and sum of logistics and unmet demand penalty costs simultaneously rather than individually | Comparison of solutions obtained by the proposed bi-objective model and the two single-objective models |
| Rottkemper et al. (2011) | – | Burundi | Meningitis epidemic | Médecins Sans Frontièères | Taking the possibility of future disruptions (e.g. an overlapping disaster) into account can help to balance inventories and decrease unsatisfied demand, without a significant increase in logistics costs | Comparison of the solutions obtained by the proposed model and the transshipment model when uncertain parts of demand are ignored |
| Salmerón and Apte (2010) | – | – | Hurricane | Fritz Institute, Federal Emergency Management Agency (Heidtke, 2007; Tean, 2006) | As more budget becomes available, allocation levels increase progressively in warehouses and shelters, and remain fairly constant for expansion of ramp space and health facilities, what is a clear indication that initial conditions in warehouse capacity are the most compelling limitation to minimize casualties | Sensitivity analysis on the total budget |
| Sheu and Pan (2014) | 2009 | Taiwan | Typhoon | Ministry of the Interior Department of Statistics, Pingtung County Government, Central Disaster Emergency Operation Center Taiwan, local hospitals, GIS information | A centralized emergency supply network designed by the proposed method is superior over a decentralized one (host government does not collaborate with local NGOs), especially with regard to distribution network design | Comparison of the solutions obtained by the model under a decentralized and centralized condition |
| Uichanco | 2013 | Phillipines | Typhoon | Philippines’ Department of Social Welfare and Development, National Disaster Risk Reduction and Management Council, Federal Emergency Management Agency, GIS information (Holland, 1980) | The proposed robust model yields a lower average distance traveled than other common pre-positioning methods under typhoon path uncertainty and supply vulnerability | Comparison of the solutions obtained with no pre-positioning and the nominal, stochastic and robust pre-positioning strategies |
| Article | Time | Location | Disaster type | Principal data sources | Main findings | Methodology |
|---|---|---|---|---|---|---|
| 1900–2006 | Worldwide | Earthquake | National Geophysical Data Center, Socioeconomic Data and Applications Center | Pre-disaster investments have a strong impact on the emergency strategy response time and proportion of demand satisfied | Sensitivity analysis on pre- and post-disaster budgets | |
| – | Tehran | Earthquake | Risk Assessment tool for Diagnosis of Urban Areas against Seismic Disaster, GIS information | Virtual zoning approach (that helps the authorities create a better collaboration between neighboring local areas) yields lower logistics costs and greater coverage that municipal subregional zoning | Comparison of the solutions obtained by the two different approaches | |
| 2005 | US Gulf Coast | Hurricane | US Coast Guard, National Weather Service, National Hurricane Center | Pre-positioning of repair supplies increases the ability of a port to quickly recover from disasters | Sensitivity analysis on the amount of available supply | |
| – | Tehran | Earthquake | Tehran Municipality Urban Planning and Research Center | It is beneficial to consider the total cost and people satisfaction simultaneously rather than individually; the same holds for different types of uncertainty | Comparison of the solutions obtained by the proposed bi-objective model and the two single-objective models; and models that consider some or all types of uncertainty | |
| – | South Eastern US | Hurricane | Louisiana Homeland Security and Emergency Preparedness ( | Pre-positioning emergency supplies reduces both the logistics costs and the unmet demand | Comparison of the solutions obtained with or without a pre-positioning policy | |
| – | São Paulo State | – | Historical data and geographic information | Stochastic model generates more robust solutions | Comparison of the solutions obtained by a deterministic and stochastic model | |
| – | – | – | Random instance | Stochastic programming on average reduces the logistics costs and the unmet demand compared to a simple expected value approach | Comparison of the solutions obtained by a deterministic and stochastic model | |
| 2005 | US Gulf Coast | Hurricane | National Hurricane Center, Internal Revenue Service e-File, Natural Resources Defense Council ( | Pre-positioning reduces the logistics costs | Sensitivity analyses on the amplifying factor that indicates the number of times that the distribution costs are amplified after the disaster | |
| 2009–2010 | Ethiopia | Food insecurity | World Food Program Ethiopia | The proposed model is a useful tool for food aid supply and distribution planning | Comparison of the solution obtained by the proposed model to the strategy carried out by World Food Program | |
| 1964–2012 | North Carolina | – | North Carolina Emergency Management Division, Federal Emergency Management Agency | The proposed solution approach is useful for tackling humanitarian relief problems | Comparison of the solution obtained by the proposed solution approach with the existing network design | |
| 1880–2007 | US Gulf Coast | Hurricane | North Atlantic Hurricane Database, State Government of Louisiana, American Red Cross ( | The proposed model and solution procedure can be used to efficiently solve the sheltering network planning | Demonstration of the solution algorithm convergence and efficiency for a real-world problem instance | |
| – | US Gulf Coast | Hurricane | Expected performance of the proposed pre-positioning strategy is more effective than the wait-and-see approach | Comparison of the solutions obtained using the two approaches | ||
| 2011 | Thailand | Flood | Department of Disaster Prevention and Mitigation, Ministry of Interior of the Royal Thai Government | The proposed model can be used to optimize the facility locations and stocking decisions | Sensitivity analysis on different time and cost parameters | |
| – | Seattle | Earthquake | Cascadia Region Earthquake Workgroup, Earthquake Engineering Research Institute, Washington Military Department Emergency Management Division | The proposed model can aid interdisciplinary agencies to both prepare and respond to disasters | Application of the model to solve the case study | |
| 2011 | Rio de Janeiro State | Flood | The Emergency Events Database, Instituto Brasileiro de Geografia e Estatística, International Federation of Red Cross and Red Crescent Societies ( | The integration of decisions in a multi-period context and the option of reusing vehicles reduce total costs, thus improving the overall performance of the relief operations | Comparison of the solutions obtained by a single- and multi-period model, and with our without reusing the vehicles | |
| – | Los Angeles County | Anthrax attack | The proposed heuristic can be used to locate facilities to address large-scale emergencies | Comparison of the solutions obtained by the proposed locate-allocate heuristic and simulated annealing procedure introduced in ( | ||
| – | US Gulf Coast | Hurricane | The optimal location and allocation policies change with respect to the risk parameters that describe the level of conservativeness and risk-averseness | Sensitivity analysis on the risk parameters | ||
| – | US Gulf Coast | Hurricane | The network and deprivation cost structure affects the preparedness and response planning decisions and resulting costs and level of service | Comparison of the solutions obtained by considering a single pre-selected potential supply point and the proposed three-echelon network structure with multiple supply points; and of the solutions obtained by minimizing constant, linearly- and the proposed exponentially-increasing deprivation costs | ||
| – | US Gulf Coast | Hurricane | The Atlantic Oceanographic and Meteorological Laboratory | The proposed Lagrangian L-shaped heuristic can be used as an effective large-scale resource pre-positioning planning tool | Comparison of the solutions obtained by the proposed heuristic and CPLEX | |
| – | US Gulf Coast | Hurricane | Adding the service quality constraints to the model results in more open facilities, reduced tendency of facilities to be specialized for storage of just one commodity and increased computational complexity | Comparison of the solutions obtained with and without service quality constraints | ||
| – | Istanbul | Earthquake | Istanbul Metropolitan Municipality, Japan International Cooperation Agency | Adding a probabilistic constraint that ensures a certain service reliability opens storage facilities at safer but more distant locations from their assigned affected areas, thereby reducing the expected unsatisfied demand and increasing the average distance | Comparison of the solutions obtained by employing a model with and without reliability constraints | |
| – | Seattle | Earthquake | It is beneficial to consider the response time and sum of logistics and unmet demand penalty costs simultaneously rather than individually | Comparison of solutions obtained by the proposed bi-objective model and the two single-objective models | ||
| – | Burundi | Meningitis epidemic | Médecins Sans Frontièères | Taking the possibility of future disruptions (e.g. an overlapping disaster) into account can help to balance inventories and decrease unsatisfied demand, without a significant increase in logistics costs | Comparison of the solutions obtained by the proposed model and the transshipment model when uncertain parts of demand are ignored | |
| – | – | Hurricane | Fritz Institute, Federal Emergency Management Agency ( | As more budget becomes available, allocation levels increase progressively in warehouses and shelters, and remain fairly constant for expansion of ramp space and health facilities, what is a clear indication that initial conditions in warehouse capacity are the most compelling limitation to minimize casualties | Sensitivity analysis on the total budget | |
| 2009 | Taiwan | Typhoon | Ministry of the Interior Department of Statistics, Pingtung County Government, Central Disaster Emergency Operation Center Taiwan, local hospitals, GIS information | A centralized emergency supply network designed by the proposed method is superior over a decentralized one (host government does not collaborate with local NGOs), especially with regard to distribution network design | Comparison of the solutions obtained by the model under a decentralized and centralized condition | |
| 2013 | Phillipines | Typhoon | Philippines’ Department of Social Welfare and Development, National Disaster Risk Reduction and Management Council, Federal Emergency Management Agency, GIS information ( | The proposed robust model yields a lower average distance traveled than other common pre-positioning methods under typhoon path uncertainty and supply vulnerability | Comparison of the solutions obtained with no pre-positioning and the nominal, stochastic and robust pre-positioning strategies |
Note: In most of the literature on the pre-positioning problem, the authors process a lot of data from a few databases in order to generate a single case study that they use to obtain their findings
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