Abstract
Earthquake relief network involves storage and distribution of relief aid to people in need. In this paper, a new stochastic multi-objective mixed integer mathematical model is developed and implemented in Kadikoy municipality of Istanbul, Turkey in order to configure part of the earthquake relief network. The aim of the model is to help decision makers decide on the locations of storage areas for shelters pre-earthquake and distribution of shelters from these areas to temporary shelter areas post-earthquake while minimizing earthquake scenario-specific total expected distribution distance, total expected earthquake damage risk factor of storage areas and expected total penalty cost related to unsatisfied demand at temporary shelter areas, simultaneously. In the model, storage area capacity and coverage distance restrictions are taken into consideration. The data related to potential storage areas and shelter locations were obtained from Kadikoy municipality of Istanbul and Istanbul Metropolitan Municipality (IMM). The earthquake damage risk factors were determined based on possible earthquake scenarios given in Japan International Cooperation Agency’s (JICA) report. Four event scenarios with two different earthquake scenario likelihoods were considered and sample efficient solutions from the Pareto frontier were obtained implementing the normalized (scaled) weighted sum method.
Highlights
Today’s one of the prominent problems is natural disasters such as earthquakes
A stochastic multi-objective mixed integer mathematical model for the location and distribution decisions in an earthquake relief network was developed. e model includes some aspects that can be seen in the literature; none of the existing models in the relief network literature simultaneously include decisions related to: pre-earthquake shelter storage at storage areas (SA) and post-earthquake shelter distribution from SA to temporary shelter areas (TSA) while taking into consideration storage capacity restrictions, earthquake scenario dependent demand, earthquake damage risk factor of SA, and coverage distance restrictions
E model was implemented in a pilot area; Kadikoy municipality of Istanbul, Turkey and based on these results, suggestions were made to the municipality
Summary
Today’s one of the prominent problems is natural disasters such as earthquakes. An accurate prediction of earthquakes is not yet possible, it is possible to plan the stages of disaster operations management (mitigation, preparedness, response and recovery) based on several earthquake scenarios. Rawls and Turnquist [21] presented a two-stage stochastic optimization model to pre-position various kinds of emergency supplies in storage areas pre-hurricane and to ship those to demand points post-hurricane and presented a case study focusing on the hurricane threat in the Gulf coast of US In their model, they considered scenario-based demand locations, quantities, and transportation capacities, and minimized the total cost which includes fixed cost of opening storage facilities, acquisition cost for items, expected cost of shipment to demand points, expected holding cost of unused items and expected penalty cost for shortage at demand points. Locations of shelter storage areas (SA) are determined from potential SA and shelters are distributed from the SA to temporary shelter areas (TSA), taking into consideration earthquake scenario dependent demand, earthquake damage risk factor of storage areas, coverage distance and storage capacity restrictions. To determine representative e cient solutions of the problem from the Pareto frontier, a group of 16 dispersed weight vectors were generated in Table 6, where > 0 are the weights
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