MCAL model

The suggested service areas generated by Multi-Capacities Ambulance Location Model(MCAL) automatically as in film.

This automated procedure was written in Delphi with MapInfo as GIS graphic manager. MCAL model was announced in PCSI 2009 and indexed in EI.

Multi-Capacities Ambulance Location Model 

Dah-Ming Shiah

Graduate School of Architecture and Urban Design

Chaoyang University of Technology

Taichung County, Taiwan

dave0104@gmail.com

Chin-Tun Hung

Department of Healthcare Administration

Central Taiwan University of Sciences and Technology

Taichung City, Taiwan

cthung@ctust.edu.tw

Shu-Wen Chen*

Department of Nursing

Central Taiwan University of Sciences and Technology

Taichung City, Taiwan

swchen@ctust.edu.tw

Abstract—Multi-capacities ambulance location model (MCAL) is a different type of capacitated location model compared with ambulance location/relocation model existed. It considers three different types of capacities: traveling distance, address point (population) and EMS incident locations, into one model to solve the set covering of ambulance service area location problem. MCAL is not only a deterministic model but also a probabilistic model by the definition of Brotcorne [1], since we include both types of dataset as the capacity constraints. MCAL modified the procedures from the predecessor AACM Shiah [2, 3] so that the covering procedures can be simpler, more flexible and more efficient. The results of these procedures show that it can work on irregular surface shape or fragments of sets so as to makes it more adaptable to real world.

Keywords-ambulance location/relocation model; capacitated facilities; set covering; GIS; placing algotithm

台中市歷年人口重心變化

 

臺灣歷年人口重心變化

台中市歷年人口重心往西移，而臺灣則往北移。

由專科門診的空間分佈探討醫療科別分科方式

本研究旨在發覺醫療市場的自由競爭下，對各醫療科別在醫師與病患的互動，反映在醫療市場的空間分佈結果，並依此發現，評論現行醫療科別分科的方式是否有可合併與修正的空間。本研究主要的假設，認為門診開業醫師在選擇開業地點時，會考慮相近科別的競爭，也同時會考慮病患市場的分佈，因此，同行競爭與人口大小成為最重要的關鍵。本研究就是在醫療行為自由競爭，及門診點空間的分佈的條件下，嘗試定義出不同醫科間，有同行性質的關係，但是僅以現況空間分佈的結果，做為分析的依據。

Ambulance placing problems

I suggest a new Emergency Management Hot Spots (EMHS) model as the patches of current EMS spatial distribution. This model works differently from the existing ambulance location and relocation models. EMHS consists of two parts; the first part is to identify the hot spots based on the experiences. The second part is the algorithm that selects the places for the new patches of mobile ambulance service areas. In the first part, we use the Cross Table, ANOVA, Correlation Coefficients, and grouping of factors to find special characters and patterns in the EMS records from Taichung city in the past decade. Then, we design an EMS problem locations selection method to determine the hot spots of EMS that could be the potentially busy areas of EMS requests. In the second part, we suggest a method to arrange the service areas to cover all the problem areas. With this method, we believe that it can assist the ambulance redeployment or reschedule schema and at the same time can solve the business problems in the study area. The results show that in 2008 the hot spots are 113 cells and in 2020 are 105 within Taichung city. In 2008, the study area needs five additional mobile ambulance sties to cover most of the problem areas. The pattern of hot spots shifts toward north and west of the city from 2008 to 2020, on the other hand, on the south side and the center of the city the hot spots are receding. These results can profit by location evaluation, deployment scheduling, and management of ambulances in the future.