Location optimization of emergency medical facilities for public health emergencies in megacities based on genetic algorithm

Abstract

PurposeThe purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.Design/methodology/approachUsing Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.FindingsComparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.Research limitations/implicationsFirst, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.Practical implicationsThe results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.Social implicationsThe results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.Originality/valueThe research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

“…Relevant studies on model-based methods considered EMVs' travel times in a specific time period as either a constant or a random value. Model-based methods were primarily used for emergency infrastructure site planning and EMV dispatching [28]. While these methods can reduce model complexity, they cannot fully capture the vehicle dynamics and features of EMVs and time-dependent features of road traffic flows.…”

“…Relying on overly strong model assumptions [28] and ignorance of the different characteristics of EMVs and SVs will significantly reduce the accuracy of models and algorithms, causing a disconnect between the optimized results and the real-world requirements. In the context of the big data era, future research should fully leverage the advantages of data and concentrate on uncovering the authentic rescue demand characteristics [26] and routing preferences [14,51,52] by collecting and mining actual EMV data (e.g., trajectory data, alarm data from the emergency department) to close the divide between the proposed algorithm and its real-world implementation [27,36].…”

An improved genetic algorithm with density-guiding operator based on the location selection model of emergency logistics center

“…The genetic algorithm optimization of location selection is usually realized by using operators. Liu [14] proposed a location-selection model for emergency medical centers in response to large public health events. By using the adaptive crossover operator, the convergence rate of optimization iterations is higher and the number of iterations is lower.…”

Nature-Inspired Optimization: Optimizing Distance of Emergency Response Wagons en Route to Railway Crossing Accident Location

“…Aside from this, maintaining an optimized schedule for emergency rescue wagons could maximize productivity and avoid major impact of train accidents [7]. Liu, Li [8] indicates that among the factors that influence the selection of a location to place an emergency medical facility is the new facility's capacity. For instance, it has been applied in siting emergency humanitarian facility [9] and also to group patients per county [10].…”