Study on site selection of emergency medical facilities for rural public health emergencies

In order to improve the scientificity of site selection decision⁃making of emergency medical facilities for rural public health emergencies, based on the characteristics of public health emergencies with rapid spread and strong harmfulness of corona virus disease 2019(COVID-19), according to the design standards of emergency medical facilities, taking into account the characteristics of small rural medical budget and rugged emergency roads, firstly, six influencing factors of engineering geological conditions, unit cost, infection rate, arrival time, site scale and service coverage area of alternative sites of facilities were selected. The Entropy value method(EVM) method and analytic hierarchy process(AHP) method were effectively combined to determine the weight of influencing factors. Secondly, a multi⁃objective location model considering the minimum sum of the distance from patients to emergency medical facilities and the optimal comprehensive evaluation value of the selected emergency medical facilities was established. Then, an IPSO algorithm was designed to solve the model and get the location decision. Finally, some villages in Tianmen city were selected for empirical analysis to verify the effectiveness of the model algorithm. The results show that infection rate and unit cost are the main influencing factors for the construction of emergency medical facilities. IPSO algorithm selects three emergency medical facilities, which can meet the treatment needs of patients in eight villages, and ensure that patients can seek medical treatment within 4-7 minutes,providing guarantee for efficient epidemic prevention and control activities. © 2023 China Safety Science Journal. All rights reserved.

Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency

The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%. © 2023 by the authors.

Study on site selection of emergency medical facilities for rural public health emergencies

In order to improve the scientificity of site selection decision⁃making of emergency medical facilities for rural public health emergencies, based on the characteristics of public health emergencies with rapid spread and strong harmfulness of corona virus disease 2019(COVID-19), according to the design standards of emergency medical facilities, taking into account the characteristics of small rural medical budget and rugged emergency roads, firstly, six influencing factors of engineering geological conditions, unit cost, infection rate, arrival time, site scale and service coverage area of alternative sites of facilities were selected. The Entropy value method(EVM) method and analytic hierarchy process(AHP) method were effectively combined to determine the weight of influencing factors. Secondly, a multi⁃objective location model considering the minimum sum of the distance from patients to emergency medical facilities and the optimal comprehensive evaluation value of the selected emergency medical facilities was established. Then, an IPSO algorithm was designed to solve the model and get the location decision. Finally, some villages in Tianmen city were selected for empirical analysis to verify the effectiveness of the model algorithm. The results show that infection rate and unit cost are the main influencing factors for the construction of emergency medical facilities. IPSO algorithm selects three emergency medical facilities, which can meet the treatment needs of patients in eight villages, and ensure that patients can seek medical treatment within 4-7 minutes,providing guarantee for efficient epidemic prevention and control activities. © 2023 China Safety Science Journal. All rights reserved.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

An Improved Particle Swarm Optimization Algorithm for Irregular Flight Recovery Problem

As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper. © 2022, Springer Nature Switzerland AG.

Prediction of electricity consumption during epidemic period based on improved particle swarm optimization algorithm

A prediction method of electricity consumption is developed in order to address the problems of big change and imbalance in electricity consumption caused by COVID-19. In this method, BP (Back Propagation) neural network and improved particle swarm optimization (IPSO) algorithm are combined and applied. Firstly, Pearson correlation coefficient approach is utilized to conduct data correlation analysis. Then, the BP neural network prediction model is built, and IPSO algorithm is used to optimize the neural network’s initial weights and thresholds. Considering the medical data, public opinion data, policy data and historical data of electricity consumption during epidemic period, the electricity consumption of each industry in the future is predicted. The findings suggest that the proposed model performs well in terms of prediction. The Mean Absolute Percentage Error (MAPE) for each industry’s evaluation index is 1.41%, 1.70 %, and 1.37 %, respectively. Compared with other models, the prediction accuracy is higher. By exploring the predicted results of electricity consumption during epidemic period, it is hoped that a basis prediction method of electricity consumption for power grid companies in the event of a sudden outbreak will be provided.