Mathematical Modeling of the Problem of Locating Temporary Accommodation Centers and Assigning Victims After a Possible Earthquake to Safe Places and Solving Using Meta-Heuristic Algorithms

Abstract

Introduction: Earthquakes, one of the most important natural disasters of the earth, have always caused irreparable damage to human settlements in short time. One of the most important issues that we face after an earthquake is the transfer of earthquake victims and traumatized civilians to safe places and medical centers. The city of Mashhad with different geographical faults and the presence of enormous religious, cultural, historical and industrial assets make Mashhad the most dangerous city in terms of earthquake hazards. In the 9th district of this city, the existence of worn-out structures along the narrow passages and the importance to save time in providing relief proves the need to locate temporary accommodation centers and allocate the injured to safe places.Material and Methods:The process of optimizing the accommodation of people includes 2 main steps 1) Determining candidate locations for temporary accommodation 2) Optimal allocation of population blocks (origin).The weight of criteria was calculated using the pairwise comparison method. Then suitable places for deployment are identified. Criterion in the form of giving a specific weight to each, in order to prepare the final map, is of importance. Accordingly, the opinions of experts in the field of urban crisis management have been utilized. Subsequently, using GAMS software and 7 super-innovative algorithms such as SA, PSO, ICA, ACO, ABC, FA, LAFA.Results:The average process time and cost of 7 algorithms out of ten random problems with 1000 repetitions, and an average of 10 execution times show, that the 3 algorithms ACO, ABC and LAFA have lower cost and process time than the other meta-innovative algorithms. Therefore, we use the above three algorithms to solve the case studyConclusion: Finally, the LAFA optimization algorithm had obtained a better and more appropriate result due to its execution time and cost being less than the other two algorithms.

Metaheuristics in the Humanitarian Supply Chain

“…Shavarani [44] used the NSGA-II to address a facility location problem to minimize the total relief items supply chain cost; the model type is non-deterministic. Mardaninejad and Nastaram [71] studied a facility location problem through a deterministic model to minimize the distance and fixed cost of equipping a temporary accommodation center; they address a study case and used SA, PSO, ICA, ACO, ABC, FA and LAFA algorithms. Nayeri et al [72] considers a mass evacuation problem through a multi-objective deterministic model to minimize the completion time of relief operation, using GA and PSO algorithms.…”

“…Caballero Morales et al [78] propose an MH based on K-means clustering and a micro genetic algorithm to estimate a search interval for help centers; they also use GRASP to establish 260 help centers in 3837 at-risk communities in Veracruz, Mexico. Mardaninejad and Nastaram [71], by transferring earthquake victims to safe places and medical centers by selecting candidate locations, formulate the optimal accommodation to perform using GAMS and seven metaheuristics such as Simulated Annealing and Ant Colony, among others.…”