Site Selection Models in Natural Disaster Shelters: A Review

Abstract

Constructing natural disaster shelters is important for disaster emergency management, and site selection models provide a feasible technique and method. This paper presents site selection models for natural disaster shelters. A synthesis of the types, objectives, constraints, methods of solutions, targeted disasters and applications of different site selection models for natural disaster shelters is investigated. Shelter location models can be classified as single-objective models, multiobjective models and hierarchical models, according to the objective and hierarchy type. Minimizing the evacuation distance or time, shelter construction cost or number, and the total risk are the general objectives of the models. Intelligent optimization algorithms are widely used to solve the models, instead of the Geographic Information System (GIS) method, due to the complexity of the problem. The results indicate that the following should be the main focuses of future works: How to set a model that can be applied for determining the shelter locations of multiple disasters; how to consider the uncertainty in the models; how to improve the existing algorithms or models to solve large-scale location-allocation problems; and how to develop a new resource-saving model that is consistent with the concept of sustainable development, as advocated by shelter planners and policy makers, which can be applied in real situations. This study allows those undertaking shelter location research to situate their work within the context of shelter planning.

Multi-Criteria Location Model of Emergency Shelters in Humanitarian Logistics

“…The model also aims to be a planning tool for improving the decision-making ability of disaster managers in location and allocation. Existing high subjective evaluation warehouses are included in the candidate points set, and an attempt is made to find a suitable facility location in the requisition and use of emergency humanitarian logistics [28,47].…”

Multiobjective Optimization on Hierarchical Refugee Evacuation and Resource Allocation for Disaster Management

“…e LSCP and the MCLP searches for the optimal location of ESs to cover all demands within a certain service distance. e LSCP is to minimize the total number of selected facilities as well as to minimize the construction cost, and the MCLP is to minimize the number of refugees that will not be served within the service distance [11]. However, in an actual response period, the project manager must deal with multiple conflicting objectives in the varying environment [12].…”

“…e maximum capacities of A-ESs, B-ESs, EMCs, and EDCs are limited in constraints (8)- (11). Constraints (12)- (19) reflect the number of people in each node at time t. Constraints (20)-(23) define the quantity of required resources in each node at time t; thus, constraint (24) defines the total quantity of relief resources r required from all resource demand points throughout the rescue period of 72 h. To avoid the wastage of insufficient resources, we define that the quantity of received resources of each node is no more than the quantity of required resources of each node by equation (25).…”

“…e random initialization resource allocation solution from EDCs to each resource demand points at time t is based on constraints (11) and (25). e initial PSO resource allocation solution of the ith particle (i ∈ [1, N]) ps 0 i can be represented by a four-dimensional array of all R t rch , R t rcj , R t rck , and R t rca in the model in Section 3.2. e updating method of PSO is based on equations (31) and (32).…”

“…e initial PSO resource allocation solution of the ith particle (i ∈ [1, N]) ps 0 i can be represented by a four-dimensional array of all R t rch , R t rcj , R t rck , and R t rca in the model in Section 3.2. e updating method of PSO is based on equations (31) and (32). If ps k i does not satisfy constraints (11) and (25), R t rc * ( * ∈ EMCs, A-ESs, B-ESs, and AAs) will be set to 0.…”