Rescue Points Research Articles

Heterogeneous cross domain coordinated control of ASV-AUV system for maritime search and rescue

This paper is concerned with a coordinated search and rescue problem of heterogeneous ocean vehicle cluster composed of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs) subject to model uncertainties and ocean disturbances. Firstly, a three dimensions (3D) cross domain kinematic controller with auto-stop function at the rescue points is designed for the coordinated ASV-AUV system by using a line-of-sight guidance law. Secondly, an event-triggered based communication mechanism is constructed to ensure that the ASV-AUV system can broadcast information at its trigger moments except to the auto-stop points. Finally, an adaptive motion control law based on a low-frequency learning fuzzy predictor is designed, which is able to filter out the high-frequency components of uncertainties and disturbances. The stability of the closed-loop system is proven by employing input-to-state stability and cascade stability analysis. Simulation results and numerical analysis show the effectiveness of the proposed 3D heterogeneous cross domain coordinated path following controller with auto-stop function for marine search and rescue.

Multi-objective robust optimization of staff scheduling for emergency under stochastic demand

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption.

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

A study on the route planning of aviation emergency rescue considering disaster victims splitting according to backpacks

In the emergency response phase after a natural disaster, aviation emergency rescue is an efficient means of rescue work. In this paper, the disaster victims to be rescued are discretely split according to the degree of injury, and different rescue time windows are given for each degree of injured disaster victims. The model takes into account helicopter performance factors such as capacity, cruise, hover, and fuel consumption, as well as the problem of multi-trip helicopter missions due to the scale of the emergency, with the decision objective of minimizing rescue delay loss. A genetic algorithm based encoding of virtual rescue points is developed to solve the problem, and the genetic operation of the algorithm is optimized to provide better solution performance for the model characteristics. Finally, the model and algorithm are validated with a real case, and comparative and sensitivity analyses are also performed.

Optimizing Emergency Supply Location Selection in Urban Areas: A Multi-Objective Planning Model and Algorithm

The scientific location and layout of emergency material storage and rescue points in urban areas are critical aspects of emergency management. In this study, a multi-objective programming optimization model was constructed based on related theories, incorporating multiple goal combinations with different dimensions according to various disaster scenarios and urban emergency needs. The weight factors of emergency timeliness, economy, and safety were considered, and the multi-objective model optimization problem was transformed into a single-objective comprehensive optimization model problem using the weight method. The analysis decision function was utilized to study the transformation and solution method of the urban emergency rescue point location model. Heuristic optimization algorithms were employed to perform average segmentation calculations on the preset neighborhoods, constantly changing and narrowing the neighborhood range until the algorithm termination conditions were met, approaching the domain range of the optimal solution. Additionally, another precision parameter was utilized to control the accuracy of the final solution neighborhood range. The optimization of emergency vehicle scheduling was used to synergistically solve the problem of reserve rescue point location layout and optimization solution. The results of the example demonstrate the feasibility of constructing a multi-objective model with multiple combinations of different dimensions of objectives and the rationality of the Dijkstra heuristic optimization algorithm used. This study provides multiple methodologies and alternative site selection plans for decision-makers to select the required multi-objective reserve rescue point location model based on different urban disaster situations and their own emergency rescue needs.

A Model for the Network of Relief Centers during an Earthquake in the Central Zone of Tehran, Iran

INTRODUCTION: The network of relief centers, which is a connected set of relief centers, rescue points, and rescue routes, is of utmost importance during an earthquake. This study aimed to model this network to identify the aid-receiving points and rescue routes, followed by the development of plans to improve the aid-giving process during an earthquake. METHODS: This descriptive-analytical study was conducted based on an applied research method and a quantitative approach. The required data were obtained through the library method (documents). The data used in this study included location information of relief centers (hospitals, crisis management centers, Red Crescent centers) and the transportation routes among them. These pieces of information were collected from related plans and documents. The obtained data related to the location of relief centers were analyzed using Spatial Analysis, and Network Analysis tools were utilized to analyze the transportation routes among the relief centers. FINDINGS: The findings in this study revealed the right places for receiving aid and the rescue routes that deliver aid from the aid-giving to the aid-receiving centers in the central zone of Tehran in the shortest time. Moreover, the relief centers were ranked based on their importance during a crisis. CONCLUSION: The results of this study show the number of required aid-receiving points in the central zone of Tehran through which the aid needed during a crisis can be delivered from rescue centers. The findings revealed which relief centers played a more significant role during an earthquake. The identification of these points and routes makes the provision of special planning possible during an earthquake.

Assessment of connectivity reliability and travel time of road networks after earthquake: case study of Aba Autonomous Prefecture

The road transportation infrastructure system (RTIS) provides a network of options that support the movement of people and goods. As a critical lifeline system, the resilience assessment of RTISs under the impact of different natural hazards, particularly earthquakes, has attracted extensive attention. When an earthquake occurs, an assessment of the connectivity reliability and travel time on road networks is necessary for emergency planning. In this study, the road network in the Aba Autonomous Prefecture, Sichuan Province, China, was considered as the study area and divided into 13 traffic analysis zones (TAZs) based on the administrative divisions. To consider the uncertainties related to seismic hazard assessment, random fields of ground motions were generated using a Monte Carlo simulation (MCS), considering the spatial correlation. Additionally, a connectivity reliability assessment model and travel time assessment model for the road networks were proposed. The connectivity reliability between the TAZs and increased travel time on the road networks after an earthquake were evaluated using MCS to evaluate the uncertainties related to the damage state assessment of road assets, such as bridges, tunnels, and road segments. Consequently, the results can be used as a theoretical basis for decision-making on the location and number of emergency rescue points after an earthquake and as a functional metric for resilience assessment models.

Scheduling Scheme Optimization for Emergency Resources on a Deep-sea Considering Environmental Impacts

An efficient and timely emergency response to a major accident is more challenging for the exploration and utilization of deep-sea oil and gas fields due to harsh external environment factors. An optimal solution is needed to tackle the external environment changes in the demand for emergency resources. To effectively balance emergency response time and the satisfaction of resource scheduling quantity, this paper proposes a multiple rescue points and multi-objective optimization model to address such challenges. Such model takes into account the environment factors involving wind speed and wave height caused by the variability of the deep-sea monsoon climate. Additionally, to handle the uncertainty of the resource demand at the accident sites, a linear programming and heuristic hybrid algorithm is proposed to help decision makers select the ideal routes and minimal scheduling time of deep-sea emergency resources, as well as the optimal operation season. A case study of a blowout accident is conducted to demonstrate the application of the proposed model and the real-world implications.

An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory

Emergency decision making and disposal are significant challenges faced by the international community. To minimize the emergency casualties, and reduce probable secondary disasters, it is necessary to immediately dispatch rescuers for emergency rescue in calamity prone areas. The abruptness, destructiveness, and uncertainty of emergencies, the rescue team often faces challenges of pressing time, scattered calamity locations, and diverse tasks. This necessitates the effective organization of rescuers, for their swift dispatch to the areas requiring rescue. A valuable research problem is how to group and dispatch rescuers reasonably and effectively according to the actual needs of the emergency rescue task and the situation to achieve the best rescue effect. This study establishes a dispatch model for rescuers in multiple disaster areas and rescue points. First, this paper combines the Dempster–Shafer theory (DST) and linguistic term set, to propose the concept of an evidential linguistic term set (ELTS), that can flexibly and accurately describe the subjective judgment of emergency decision-makers. It not only lays a theoretical foundation for establishing the rescuers’ dispatch model, but also aids in expressing information in uncertain linguistic environments of decision-making and evaluation. Second, to determine the weight of ability-based rescuer evaluation criteria, this study adopted the evidential best–worst method, combining it with DST to compensate for the limitations of the traditional weightage calculation method in expressing uncertainty. Third, to effectively dispatch rescuers to multiple disaster areas, modeling is carried out based on the above methods to maximize the competence of rescuers and the satisfaction of rescue time, and the best scheme for the allocation of rescuers is determined by solving the model. Finally, the advantages of the constructed model in emergency multitasking group decision-making are demonstrated through an empirical analysis.

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization.

Western China is a sparsely populated area with dispersed transportation infrastructure, making it challenging to meet people’s accessibility and mobility requirements. Rescue efficiency in sparse networks is severely hampered by the difficulty rescue teams have in getting to the scene of abrupt traffic accidents. This paper develops a layout optimization model for multiple rescue points to address this issue, suggests an improved particle swarm algorithm by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and designs a MATLAB program using an adaptive variation algorithm. The proposed approach increases the effectiveness of rescue in a sparse network and optimizes the distribution of emergency resources.

The Permanent German Forest Rescue Point System—Concept, Geographical Analysis, and Optimisation

Working in the forest is dangerous, especially in the case of motor manual work, which represents a significant proportion of all forest work in Germany. Nevertheless, many accidents in the forest also happen during recreational activities. In the unfortunate case of an accident, rapid assistance is therefore crucial. For provision of this service, a network of 63,718 high quality permanent rescue points has been established in Germany, known to all persons involved in rescue operations. These can serve as meeting points and points of orientation. The question emerges whether the number of points is sufficient and whether they are well distributed and ideally placed for conducting a rescue in the forest. Therefore, this study aims to provide a granular complex geographic analysis of the indices of distribution and the accessibility of existing rescue points. These analyses form the basis for improving and optimising the existing permanent rescue point system at federal states and the federal level. This study has developed, calculated, analysed, and assessed nine different spatial indices, which can serve as a basis for decision making in the evaluation and adaptation of the system. The indices reveal considerable federal states differences in the designation of rescue points, in terms of number, density, and distribution.

Региональные особенности частоты ДТП на трассах республиканского значения Казахстана с учетом показателей смертности и числа пострадавших за 2021 год

The purpose of the study: to study the frequency of road accidents on highways of national significance for 2021, based on the mortality rates and the number of victims in the regions of Kazakhstan.Methods. The paper analyzes the indicators of the frequency of accidents, their mortality rate and the number of victims, based on the population density. The work of the country's highway medical and rescue centers has been also evaluated, namely the number of departures and the number of hospitalizations.Results. The number of road accidents in relation to the population density during the study period prevails in the city of Almaty and Almaty region – 33%, Zhambyl region – 11%, in the city of Nur-Sultan and Akmola region - 11%, in the city of Shymkent and Turkestan region – 7%. The proportion of accidents in the context of the regions of Kazakhstan left: the southern region – 54%, the northern region – 22%, the western region – 12%, the eastern region - 6%, central Kazakhstan – 5%. Greater number of victims per 1 exit of highway medical and rescue points can be noted in the city of Almaty and Almaty region (1211), the city of Shymkent and Turkestan regions (408), as well as in Zhambyl (412) and Karaganda (403) regions. The highest mortality rates as a result of road accidents are observed in the southern region of Kazakhstan: the city of Almaty and Almaty region (31.98 per 100 thousand population), in Shymkent and Turkestan region (16.46 per 100 thousand population) and in Zhambyl region (10.67 per 100 thousand population). The lowest indicator in the North Kazakhstan region is 2.03 per 100 thousand population.Conclusions. There is a correlation between the cases of road accidents, the number of injured and dead as a result of road accidents and the population density, since the largest proportion of road accidents occur in the densely populated regions of Kazakhstan. There is a decrease in the number of road accidents in 2021 by 16% compared to previous years, which was reflected in a decrease in the number of visits by teams of HMRP by 12%, medical care provided by 27% and the number of hospitalized by 8%

Methodology of emergency medical logistics for multiple epidemic areas in public health emergency.

Coronavirus disease 2019(COVID-19) has brought great disasters to humanity, and its influence continues to intensify. In response to the public health emergencies, prompt relief supplies are key to reduce the damage. This paper presents a method of emergency medical logistics to quick response to emergency epidemics. The methodology includes two recursive mechanisms: (1) the time-varying forecasting of medical relief demand according to a modified susceptible-exposed-infected- Asymptomatic- recovered (SEIAR) epidemic diffusion model, (2) the relief supplies distribution based on a multi-objective dynamic stochastic programming model. Specially, the distribution model addresses a hypothetical network of emergency medical logistics with considering emergency medical reserve centers (EMRCs), epidemic areas and e-commerce warehousing centers as the rescue points. Numerical studies are conducted. The results show that with the cooperation of different epidemic areas and e-commerce warehousing centers, the total cost is 6% lower than without considering cooperation of different epidemic areas, and 9.7% lower than without considering cooperation of e-commerce warehousing centers. Particularly, the total cost is 20% lower than without considering any cooperation. This study demonstrates the importance of cooperation in epidemic prevention, and provides the government with a new idea of emergency relief supplies dispatching, that the rescue efficiency can be improved by mutual rescue between epidemic areas in public health emergency.

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state‐of‐arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).

Optimization of distribution of emergency resources for emergency rescue points of oil and gas pipelines

According to the inherent characteristics of long-distance oil and gas pipelines, the optimization of emergency resources allocation can be implemented to maximize the utilization of pipeline emergency resources under a certain cost of emergency investment. We built an improved solution of a multiple knapsack problem in a greedy algorithm, proposed maximizing Emergency Resources Factor (ERF) as the greedy strategy, and established the optimization model of emergency resources allocation. This model innovatively combines factors such as the centrality of rescue points, the risk of pipe sections, and the necessity of emergency resources. The results show that, compared to a conventional resource allocation in a fixed proportion, an optimized allocation can reduce resource shortage and redundancy by 2.660% and 1.051%, respectively. Therefore, this model can be used to control the initialization of resource allocation in emergency rescue points of long-distance oil and gas pipelines.

The optimal allocation of emergency materials for multiple points on water based on genetic algorithm

In order to study the shortest time of emergency supplies allocation, the supply chain of rescue materials is divided into three levels that is supply areas, shore rescue points and multiple accident points. Taking the limited period of emergency supplies as a prerequisite, a material distribution model is built for studying the shortest time of material distribution from rescue base to accident site; considering the sustainability of rescue base supplies, a land material distribution model is built for studying the minimum cost of transportation problem. According to the dispatching priority of emergency supplies at each accident site, a genetic algorithm is designed to solve it, and a approximate optimal rescue material distribution plan is obtained.

Emergency material location-allocation planning using a risk-based integration methodology for river chemical spills.

It is critical for emergency material preparedness in the pre-accident phase to provide location-allocation planning and improve rescue capacity in an effective emergency response time due to increasing frequency of river chemical spills. In this study, an effective two-stage evaluation and selection framework is developed integrating fuzzy multi-criteria decision-making (MCDM) method and multi-objective optimization model to obtain the optimal emergency material location-allocation (EMLA) scheme for coping with river chemical spills. In the evaluation stage, the emergency material warehouse alternatives are evaluated by a fuzzy TOPSIS method based on environmental risk assessment. In the selection stage, the EMLA optimization scheme is identified by a multi-objective optimization model to allocate emergency materials for all the risk sources in a time-effective manner. The two-stage evaluation and selection framework is then applied in Jiangsu province, China. The EMLA optimization scheme finally selects the best five emergency material warehouses (WZ1, WZ 4, WZ 5, WZ 18, and WZ 25) for Jiangsu province with the relative closeness 0.6014, 0.4676, 0.5179, 0.3360, and 0.2935, respectively. The EMLA results demonstrate that the developed framework could obtain EMLA optimization scheme with the objective of minimum emergency rescue points and maximum integrative rescue abilities and provide all the risk resources emergency materials in a quick response for river chemical spills in the pre-accident phase.

An Internet of Things based material delivery model for disaster management in libraries

PurposeBased on the context of the Internet of Things (IoT), the territorial public emergency supplies will be networked, platform-based management, unified emergency dispatch. The problem of supplies dispatching in the “last kilometer” of emergency is solved, and the supplies needed in the disaster area are promptly delivered to the hands of the victims so that they can quickly be rescued after the disaster and to save valuable time for rapid rescue, which can greatly decrease casualties and property losses. This paper aims to discuss these issues.Design/methodology/approachBy analyzing the shortage of existing emergency supplies dispatching research and taking all factors such as disaster area demand, social reserve, road conditions, mode of transport, loading limit, disaster area satisfaction rate and road capacity into consideration under the background of IoT, a variety of the territorial emergency supplies dispatching model with more rescue points, more affected areas are constructed. The objective function of the model is to aim in finding the shortest rescue time, giving the solution algorithm, and finally simulating the simulation case.FindingsBased on the context of the IoT, the territorial public emergency supplies will be networked, platform-based management, unified emergency dispatch. Considering factors such as road conditions, modes of transport and road capacity, the authors construct a number of emergency rescue plans, multiple disaster scenarios and various emergency supplies dispatching models. The authors simulate the situation through simulation cases with the shortest time being the ultimate goal. The problem of supplies dispatching in the “last kilometer” of emergency is solved, and the supplies needed in the disaster area are promptly delivered to the hands of the victims so that they can quickly be rescued after the disaster and to save valuable time for rapid rescue, which can greatly decrease casualties and property losses.Originality/valueThis paper provides little research on the dispatch of emergency supplies. The problems of direct dispatch from the rescue point to the affected area and dispatch of supplies without relying on the arrival of emergency supplies at the rear are addressed. Therefore, this study does not focus on the arrival of emergency supplies at the rear but on direct dispatching issues during territorial public emergency supplies from the rescue point to the disaster point.

Multi-Agent Aviation Search Task Allocation Method

The multi-agent technology is used to reasonably allocate the search area determined after the aircraft crash, and the research on the task assignment problem of aircraft crash area based on multi-agent system is proposed. In the simulation experiment, the model of multiple rescue points, single disaster points and multiple emergency resource allocations is shown. The results show that the method can optimize the allocation of the search task in the aircraft crash area and dynamically change with the change of the crash area. The aircraft’s search-assisted decision-making system provides some support.

Reachability guarantee based model for pre-positioning of emergency facilities under uncertain disaster damages

Many practices shown that appropriate locations of emergency facilities play important roles both in pre-disaster service and in post-disaster relief. However, damage to the transport network can adversely affect accessibility between emergency facilities and rescue points, thereby hampering timely rescue operations. To lighten this adversely affection, we proposed a multi-objective optimization model for pre-positioning of emergency facilities based on the concept of the minimum reachability guarantee. This concept was defined as the maximum damage that the road network can withstanding. One goal of the model is to maximize the minimum reachability guarantee for post-disaster relief and another goal is to minimize the operating cost for normal service. These two goals are modeled as the max-flow problem and the shortest path problem, respectively. We embedded these two problems into the p-center facility location problem to avoid the bi-level structure of the model. The methodology is applied to the Sioux Falls transportation network to illustrate the trade-off between the minimum reachability guarantee and the operating cost. In addition, by comparing the number of unreachable rescue points in the random disaster scenarios, the effect of the minimum reachability guarantee in the optimization model is proved.