Location-allocation modeling for emergency evacuation planning with GIS and remote sensing:A case study of Northeast Bangladesh

This work developed models to identify optimal spatial distribution of emergency evacuation centers(EECs)such as schools,colleges,hospitals,and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh.The use of location-allocation models(LAMs)for evacuation in regard to flood victims is essential to minimize disaster risk.In the first step,flood susceptibility maps were developed using machine learning models(MLMs),including:Levenberg-Marquardt back propagation(LM-BP)neural network and decision trees(DT)and multi-criteria decision making(MCDM)method.Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic(AUROC)curve.Mathematical approaches in a geographic information system(GIS)for four well-known LAM problems affecting emergency rescue time are proposed:maximal covering location problem(MCLP),the maximize attendance(MA),p-median problem(PMP),and the location set covering problem(LSCP).The results showed that existing EECs were not optimally distributed,and that some areas were not adequately served by EECs(i.e.,not all demand points could be reached within a 60-min travel time).We concluded that the proposed models can be used to improve planning of the distribution of EECs,and that application of the models could contribute to reducing human casualties,property losses,and improve emergency operation.

Integration of Location-Allocation and Accessibility Models in GIS to Improve Urban Planning for Health Services in Al-Madinah Al-Munawwarah, Saudi Arabia

In this study, accessibility and location-allocation models have been integrated into GIS to improve spatial planning and environmental sustainability of health services in Al-Madinah Al-Munawwarah. This integration provides a planning framework in order to check the efficiency of the spatial allocation of health services and to generate alternatives either by proposing an active service or to improve an existing one. To achieve these objectives, the accessibility to the service area was analyzed within the analysis of health services networks, which are divided into eight types: public hospitals, specialized hospitals, health units, healthcare centers, infirmaries, clinic complexes, the Red Crescent Center, and ambulance facilities, with time intervals of (5 minutes - 10 minutes - 15 minutes) to access coverage ranges, and the location-allocation model was used based on the maximum coverage model within a response time not exceeding 15 minutes, The results of the study revealed the poor distribution of health services Al-Madinah Al-Munawwarah suffers from weak accessibility to health services coverage areas and is unable to meet the needs of its population at present. The current need for health services reached twenty-four locations, including two public hospitals, three specialized hospitals, two health centers, three ambulance facilities, four infirmaries, three clinic complexes, four health units, and three Red Crescent centers.

The Capacitated Location-Allocation Problem in the Presence of <i>k</i>Connections

We consider a capacitated location-allocation problem in the presence of k connections on the horizontal line barrier. The objective is to locate a set of new facilities among a set of existing facilities and to allocate an optimal number of existing facilities to each new facility in order to satisfy their demands such that the summation of the weighted rectilinear barrier distances from new facilities to existing facilities is minimized. The proposed problem is designed as a mixed-integer nonlinear programming model. To show the efficiency of the model, a numerical example is provided. It is worth noting that the global optimal solution is obtained.

Experiment Design for the Location-Allocation Problem

The allocation of facilities and customers is a key problem in the design of supply chains of companies. In this paper, this issue is approached by partitioning the territory in areas where the distribution points are allocated. The demand is modelled through a set of continuous functions based on the population density of the geographic units of the territory. Because the partitioning problem is NP hard, it is necessary to use heuristic methods to obtain reliable solutions in terms of quality and response time. The Neighborhood Variable Search and Simulated Annealing heuristics have been selected for the study because of their proven efficiency in difficult combinatorial optimization problems. The execution time is the variable chosen for a factorial experimental design to determine the best-performing heuristics in the problem. In order to compare the quality of the solutions in the territorial partition, we have chosen the execution time as the common parameter to compare the two heuristics. At this point, we have developed a factorial statistical experimental design to select the best heuristic approaches to this problem. Thus, we generate a territorial partition with the best performing heuristics for this problem and proceed to the application of the location-allocation model, where the demand is modelled by a set of continuous functions based on the population density of the geographical units of the territory.

The Impact of Residential and Non-Residential Demand on Location-Allocation Decision-Making: A Case Study of Modelling Suitable Locations for EMS in Leicester and Leicestershire, England UK

The research examines the impact of residential and non-residential demand on facility location planning by comparing results from two location models: travel-to-work (TTW) and Residential model. The TTW model considers short-term changes in the state of the population due to travel-to-work (non-residential demand). By contrast, the Residential model uses a static snap-shot of the population based on official census estimates (residential demand). Comparison of both models was based on a case study of Emergency Medical Services (EMS) location-allocation planning problem in Leicester and Leicestershire, England, UK. Results showed that the using a static residential demand surface to plan EMS locations overestimates actual demand coverage, compared to a non-residential demand surface. Differences in location-allocation results between the models underscore the importance of accounting for temporal changes in the state of the population when planning locations for health service facilities. The findings of the study have implications for siting of EMS, designing, and planning of EMS service catchments and allocation of prospective demand to EMS sites. The study concludes that consideration of temporal changes in the state of the population is important for reliable and efficient location-allocation planning.

Geographical Analysis of Smoking in Leeds: A GIS-Based Location-Allocation Technique for the Optimal Location of Smoking Cessation Services

Smoking is associated with several illnesses in the UK. Smoking rate in Leeds is higher than the national average. Finding optimal locations for stop-smoking services will be a good place to start in reducing smoking rates. The study utilizes a GIS-Based location-allocation method for the optimal distribution of smoking cessation centres in relation to the spatial distribution of the smoking population in Leeds. The demand for the smoking cessation clinics was estimated based on the 2009 General Life Style (GLS) statistics on age and social class stratification of smoking rates for the UK. Leeds specific rates were then obtained from the 2001 census key statistics data on socioeconomic status and age structure for output areas via Census Area Statistics Website (CASWEB). The research findings show that spatial inequalities in smoking rate exist in output areas of Leeds. Poorer and non-skilled populations are demonstrated to have higher smoking rates compared with wealthier neighbourhoods. The study confirms the capability of GIS-Based location-allocation techniques to be useful modelling tools for determining the best locations for health facilities. The model allocates services in relation to the spatial patterns of demand in a fashion that minimises average travel distance.

Combining Geographic Information Systems for Transportation and Mixed Integer Linear Programming in Facility Location-Allocation Problems

In this study, we aimed to assess the solution quality for location-allocation problems from facilities generated by the software TransCAD&reg;?, a Geographic Information System for Transportation (GIS-T). Such facilities were obtained after using two routines together: Facility Location and Transportation Problem, when compared with optimal solutions from exact mathematical models, based on Mixed Integer Linear Programming (MILP), developed externally for the GIS. The models were applied to three simulations: the first one proposes opening factories and customer allocation in the state of Sao Paulo, Brazil;the second involves a wholesaler and a study of location and allocation of distribution centres for retail customers;and the third one involves the location of day-care centers and allocation of demand (0 - 3 years old children). The results showed that when considering facility capacity, the MILP optimising model presents results up to 37% better than the GIS and proposes different locations to open new facilities.

基于城市扩张的动态选址模型--以深圳垃圾转运站选址为例

现有的许多选址模型研究,主要集中在对选址算法的改进,而忽略了城市作为复杂地理系统所具备的不确定性和动态发展性对选址结果的影响。这往往导致最终的选址结果在设施投入使用后,无法满足新的需求甚至出现不适应新环境的情况。本文将基于GeoCA(地理元胞自动机)的城市扩张模型引入到传统的Location-allo-cation选址模型中,构建一个动态的选址模型框架,探索如何将选址结果建立在动态的、科学的城市形态预测基础上,促使选址结果更具有客观性、先见性和更符合可持续发展观,这也正是本研究的重要意义所在。同时,框架中的许多子模型都能够单独被优化,具有高度的可伸缩性,能够自适应于特定区域的选址要求。

面向需求点区域可达性的配送中心空间选址优化方法

配送中心空间选址对应急物流规划具有重要意义,配送中心的可达性直接影响灾难发生时应急救援及物资调度的效率。围绕空间选址问题,从需求点空间可达性角度,利用GIS方法提出了一种面向需求点区域可达性的配送中心候选点提取算法,并通过区位-布局(location-allocation,LA)模型实现了配送中心选址优化。该选址方法考虑了区域可达性与人口分布之间的关系,通过极值分析得到适合建立配送中心的备选区域。在此基础上,以最小设施点数和最大覆盖度为目标,筛选出最优的配送中心位置。为检验方法的有效性,以四川省茂县凤仪镇为研究区进行配送中心选址实验。结果表明,利用该方法提取的配送中心的位置及空间分布是合理、可行的,研究成果为多配送中心的空间选址优化问题拓展了研究思路。

A Modified Gravity p-Median Model for Optimizing Facility Locations

The gravity p-median model is an important improvement to the widely-used p-median model. However, there is still a debate on its validity in empirical applications. Previous studies even doubt the significance of the gravity p-median model. Using a case study of tertiary hospitals in Shenzhen, China, this study re-examines the difference between the gravity p-median model with the p-median model, by decomposing the difference between the two models into gravity rule and variant attraction. This study also proposes a modified gravity p-median model by incorporating a distance threshold. The empirical results support the validity of the gravity p-median model, and also reveal that only when the attractions of candidate facility locations are variable will the gravity p-median model lead to different results with the p-median model. The difference between the modified gravity p-median model and the gravity p-median model is also examined. Moreover, the impacts of the distance-decay parameter and distance threshold on solutions are investigated. Results indicate that a larger distance-decay parameter tends to result in a more dispersed distribution of optimal facilities and a smaller average travel time, and a smaller distance threshold can better promote the spatial equity of facilities. The proposed method can also be applied in studies of other types of facilities or in other areas.