ABSTRACT
To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is proposed. First, an improved scan and Clarke-Wright algorithm is used to obtain the optimal vehicle path at the initial time. Then, the group movement strategy is designed to generate offspring individuals with an improved quality for refining the updating ability of individuals in the population. Finally, in order to maintain population diversity, a different weights strategy is used to expand individual search spaces, which can prevent individuals from prematurely gathering in a certain area. The experimental results show that the performance of the improved whale swarm algorithm is better than that of the ant colony system and the adaptive chaotic genetic algorithm, which can minimize the cost of material distribution and effectively eliminate the adverse effects caused by the change of road conditions.
Subject(s)
Algorithms , Whales , Animals , Movement , Problem SolvingABSTRACT
Objective:To study the value of autoregressive integrated moving average (ARIMA) and autoregressive (AR) models in predicting the daily number of ambulances in prehospital emergency medical services demand in Guangzhou.Methods:Matlab simulation software was used to analyze the emergency dispatching departure records in Guangzhou from January 1, 2021 to December 31, 2021. A time series for the number of ambulances per day was calculated. After identifying the time series prediction model, ARIMA(1,1,1), AR(4) and AR(7) models were obtained. These models were used to predict the number of ambulances per day. ARIMA(1,1,1) model divided the time series into the training set and test set. Prony method was used for parameter calculation, and the demands of number of ambulances of the next few months were forecasted. AR(4) and AR(7) models used uniformity coefficient to forecast the demands of number of ambulances on that very day.Results:ARIMA(1,1,1), AR(4) and AR(7) can effectively predict the number of ambulances per day. The prediction fitting error of ARIMA (1,1,1) decreased with the extension of prediction time. The mean absolute percentage error (MAPE) of forecast results of daily vehicle output of emergency dispatching within two months was less than 6% and the predicted results were almost within the 95% confidence interval. The residual analysis of the model verified that the model was significantly effective.Conclusions:ARIMA model can make a long-term within two months and effective prediction fitting of the daily vehicle output of emergency dispatching, and AR model can make a short-term and effective prediction of the daily vehicle output of emergency dispatching.
ABSTRACT
After the november 19th 2015 Paris terrorist attacks, there was a clear need to update the Medical Intervention Plans (MIP) for Mass Casualty Events (MCE) in the Brussels Capital Region (BCR), because they only offered a response to single-site MCE in a peace-time context. We compared the organisation and the resources of the BCR and cities like Paris and Lille, we discussed with our french colleagues and formed a Multisite Attack Task-force that produced a specific multisite MIP, which had to be put to use only a few days after its creation.
Les attentats de Paris du 19 novembre 2015 ont mis en avant la nécessité de revoir le Plan d'Intervention médicale (PIM) en cas de catastrophe dans la Région de Bruxelles-Capitale (RBC) car il était axé sur des évènements survenant au niveau d'un seul site et dans un contexte de paix civile. La comparaison de l'organisation et des moyens des villes française comme Paris et Lille, la discussion avec nos collègues français et au sein du Groupe de Travail Attentats Multisites RBC ont permis d'aboutir à Bruxelles à la finalisation d'un PIM multisites qui a été mis à l'épreuve seulement quelques jours après sa création.
