MCLP and SQM models for the emergency vehicle districting and location problem

Abstract

Over time, the number of unexpected earthy, oceanic and atmospheric events is rising each year. Hence, disaster management is considered as one of the most important scientific and practical issues in developed and developing countries. Therefore, in this study, we review and develop the problem of locating the emergency units with constraints including the number of available ambulances, limited budget for deployment of ambulances and the minimum acceptable level of covering. The proposed model improves the spatial queuing model (SQM) and Maximal Covering Location Problem (MCLP) by considering the cost of the deployment of the emergency units, which makes it closer to real-world conditions. Because the proposed model is NP-hard, the model is solved using three heuristics including Simulated Annealing (SA), Genetic Algorithm (GA) and a hybrid of both. The preliminary results indicate that the hybrid method had better performance to achieve the optimal or close to optimal solution.

A Multi-Objective Optimization Method for Maritime Search and Rescue Resource Allocation: An Application to the South China Sea

“…Currently, the research field of search and rescue (SAR) resource allocation mainly focuses on urban emergency responses after natural disasters, which encompasses the selection of rescue facilities (e.g., hospitals, fire stations, emergency shelters, etc.) and the allocation of emergency resources in existing warehouses and shelters explicitly [15][16][17]. In contrast, research on the allocation of MSAR resources is limited.…”

“…In this model, the first objective is to minimize the emergency response time, as expressed in Equation (11). The second objective is to minimize the allocation cost, as expressed in Equations ( 12)- (15). The constraints are expressed in Equations ( 16)- (21).…”

“…Step 2: Compute the objective function value of each individual in the initial population according to Equations ( 11)- (15).…”

The continuous maximal covering location problem in large-scale natural disaster rescue scenes

“…Consequently, by using the surrogate constraints in Eq. ( 18) and a given accuracy requirement ε , the nonlinear Euclidean distances in Constraints ( 2), ( 6), (11), and ( 13) can be converted into the following linear Constraints ( 21), ( 22), (23), and (24), respectively, as…”

A maximal covering facility location model for emergency services within an M (t)/M/m/m queuing system

“…They formulated this problem using M/M/1 queuing system and solved the model by applying a Genetic Algorithm within the desirability function framework. Mohammadi and Mirzazadeh (2014) developed the problem of locating the emergency units with constraints including the number of available ambulances, limited budget for deployment of ambulances, and the minimum acceptable level of covering. The proposed model improved the spatial queuing model and maximal covering location problem by considering the cost of the deployment of the emergency units.…”