Emergency Resource Allocation Research Articles

A Multi-period Optimal Distribution Model of Emergency Resources for Responding to COVID-19 Under Uncertain Conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Crash severity analysis: A data-enhanced double layer stacking model using semantic understanding

The crash severity analysis is of significant importance in traffic crash prevention and emergency resource allocation. A range of innovations offers potential traffic crash severity prediction models to improve road safety. However, the semantic information inherent in traffic crash data, which is crucial in enabling a deeper understanding of its underlying factors and impacts, has yet to be fully utilized. Moreover, traffic crash data are commonly characterized by a small sample size, which leads to sample imbalance problem resulting in prediction performance decline. To tackle these problems, we propose a semantic understanding-based data-enhanced double-layer stacking model, named EnLKtreeGBDT, for crash severity prediction. Specifically, to fully leverage the inherent semantic information within traffic crash data and analyze the factors influencing crashes, we design a semantic enhancement module for multi-dimensional feature extraction. This module aims to enhance the understanding of crash semantics and improve prediction accuracy. Then we introduce a data enhancement module that utilizes data denoising and migration techniques to address the challenge of data imbalance, reducing the prediction model's dependence on large sample crash data. Furthermore, we construct a two-layer stacking model that combines multiple linear and nonlinear classifiers. This model is designed to augment the capability of learning linear and nonlinear mixed relationships, thereby improving the accuracy of predicting the severity of crashes on complex urban roads. Experiments on historical datasets of UK road safety crashes validate the effectiveness of the proposed model, and superior performance of prediction precision is achieved compared with the state-of-the-arts. The ablation experiments on both semantic and data enhancement modules further confirm the indispensability of each module in the proposed model.

A Review of Emergency Resource Allocation under Natural Disasters

The acceleration of urbanization has led to increasingly serious casualties and property losses caused by natural disasters, while also exposing issues such as unreasonable and inefficient allocation of emergency resources. This article elaborates on the location selection of urban emergency resources, prediction of disaster point demand, construction of emergency resource scheduling models, and application of intelligent optimization algorithms by consulting relevant literature. It analyzes some problems in current emergency resource research and summarizes the key points that need to be broken through in future research.

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

AbstractEfficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports.

YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI

Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

Research on the Dynamic Model of Emergency Rescue Resource-Allocation Systems for Mine-Fire Accidents, Taking Liquid CO2 Transportation as an Example

After a mine-fire accident occurs, a large number of emergency resources need to be allocated to rescue those involved in the mine-fire accident. The allocation of emergency resources for mine-fire accidents has the characteristic of being a complex system with strong uncertainty. To investigate the impact of various variables on the allocation of emergency resources in mine-fire situations, this paper analyzes the relevant factors that influence the process of allocating emergency resources during mine fires. It defines the variables of the mine-fire emergency resource-allocation system based on relevant assumptions. Causal loop and stock flow diagrams are drawn to illustrate the relationships between the variables and the system dynamics equation. Finally, a system dynamics model for mine-fire emergency resource allocation is established. The Vensim software was used to simulate the model of a mine-fire emergency rescue. The simulation produced curves for the evolution rate of the fire, the arrival rate, the demand for emergency resources, in-transit resources, arrival, and the usage of resources during the emergency. The results indicate a positive correlation between the quantity of emergency resources in-transit and the arrival rate of emergency resources: they are positively correlated with the amount of emergency-management investment. Additionally, the duration of the maximum quantity of emergency resources in-transit is positively correlated with the length of the emergency resource-allocation route. On the other hand, the evolution rate of the mine fire and the arrival rate of its emergency resources are negatively correlated with the level of emergency management. The evolution rate of the mine fire becomes larger and the damage caused by the mine-fire accident is greater when the decision-making ability of commanders is at a low level.

Emergency resource allocation considering the heterogeneity of affected areas during the COVID-19 pandemic in China

The scientific allocation of emergency resources is crucial to ensure the success of COVID-19 relief operations. However, the heterogeneity of epidemic areas has an important impact on the allocation of emergency resources. Although it is a crucial topic, there has been limited research that considers the heterogeneity of affected areas in the emergency resource allocation. To bridge the gap, this study proposes a multi-period optimal allocation model of emergency resources considering the heterogeneity of affected areas, which aims to make the allocation of resources more equitable, efficient and economical. Then, a typical and representative case of emergency medical resource allocation in Hubei Province, China (where the epidemic occurred earlier and was seriously affected by COVID-19), was selected for a simulation study to verify the effectiveness and feasibility of the proposed model and method. The study finds that considering the heterogeneity such as disaster coefficient and demand urgency in different disaster stricken areas in emergency resource allocation can minimize the negative impact of resource shortfalls, especially in the early period of relief operations with insufficient resource supply. In addition, the proposed model can optimize multi-period emergency resource allocation by simultaneously considering time (efficiency criterion), cost (economic criterion), and loss(equity criterion), which is in line with the actual needs of emergency rescue to the COVID-19 epidemic. The results of this study can be effectively applied to the multi-period optimal allocation of emergency resources for large-scale public health emergencies, and providing insights for the government and relevant management departments to formulate emergency resource allocation policies and plans.

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

Robust optimization method of emergency resource allocation for risk management in inland waterways

This study proposes a robust optimization method for waterborne emergency resource allocation in inland waterways that addresses the uncertainties and mismatches between supply and demand. To accomplish this, we integrate the risk evaluation of maritime with a robust optimization model and employ the Entropy Weighted Method (EWM)-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-Analytic Hierarchy Process (AHP) method to evaluate the risk of various areas. The approach enables exploration of the relationship between maritime risk and emergency resource allocation strategy. The robust optimization method is used to deal with uncertainty and derive the robust counterpart of the proposed model. We establish an emergency resource allocation model that considers both the economy and timeliness of emergency resource allocation. We construct an optimization model and transform it into an easily solvable robust counterpart model. The results demonstrate that the proposed method can adapt to real-world scenarios, and effectively optimize the configuration effect while improving rescue efficiency under reasonable resource allocation. Specifically, the proportion of rescue time saved ranges from 28.52% to 92.60%, and the proportion of total cost saved is 95.82%. Our approach has significant potential to provide a valuable reference for decision-making related to emergency resource allocation in maritime management.

Retracted: The Evaluation Method of Community Emergency Resource Allocation Based on Coordination Sensor Information Collection

Retracted: The Evaluation Method of Community Emergency Resource Allocation Based on Coordination Sensor Information Collection

Mitigating vulnerability of a multimodal public transit system for sustainable megacities: a real-time operational control method

The multimodal public transit system (MPTS) has been recognized as a primary mobility support system for sustainable megacities. However, it is often vulnerable to various service disruptions, and the most severe circumstance is the interdependent cascading failures interacting among urban rail transit, bus transit, and road transit networks. The vulnerability of an MPTS against this severe failure, associated with extreme performances, limits the building of future resilient megacities. In this paper, a real-time operational control method based on the system emergency capability (SEC) is developed to block the dynamic unfolding paths of this severe failure considering network topology characteristics and dynamic evolution characteristics. This method is immediately available for real-time emergency control, while previous studies on qualitative optimization strategies cannot. Remarkably, a three-stage association design process is conducted to explore the most efficient SEC loading strategy, involved with multiple intertwined influential factors, including the target loading stations, loading time-step intervals, and loading interval length and loading strength. Finally, a case simulation is undertaken to indicate the adaptability of the proposed method. This work can provide critical insights into real-world emergency resource allocation and an underlying simulator with search direction knowledge for future intelligent algorithm-based optimal control.

A method for optimizing maritime emergency resource allocation in inland waterways

Maritime safety and emergency operation management are critical to preventing maritime accidents and mitigating risks. In this study, we propose a novel method to optimize maritime emergency resource allocation to improve emergency management efficiency. We combine the analytic hierarchy process (AHP) and coefficient of variation (CV) to develop an inverse comprehensive weight (CW) - data envelopment analysis (DEA) model. We apply this model to evaluate the efficiency of allocating emergency resources among ten Maritime Safety Administrations (MSAs) in the Jiangsu section of the Yangtze River in China. Our results indicate that five MSAs have low emergency management benefits, and we propose emergency resource allocation optimization options to improve their emergency management benefits. This study provides policymakers with valuable insights and guidelines for optimizing maritime emergency resource allocation in inland waterways.

Utilizing Numerical Models and GIS to Enhance Information Management for Oil Spill Emergency Response and Resource Allocation in the Taiwan Waters

This study aims to establish a comprehensive workflow for developing emergency response plans for both actual and scenario oil spill incidents in the Taiwan waters while addressing the resource allocation for oil spill containment as well. This workflow comprises two vital components. The first component involves the integration of numerical tools and observational data, which includes the incorporation of wind data from sources such as the National Centers for Environmental Prediction (NCEP) or meteorological stations. Additionally, it incorporates ocean current data simulated by the semi-implicit cross-scale hydroscience integrated system model (SCHISM) into the general NOAA operational modeling environment (GNOME) model, which is a new approach for this purpose. In order to assess the efficacy of this component, two distinct case studies were conducted. The first case study focused on an incident in a northern coastal area of Taiwan under open sea conditions, whereas the second case study examined an incident within a major commercial harbor in central Taiwan. The second component of this workflow involves creating oil risk maps by integrating the results from the first component with specific geographical factors into Google Earth. These oil risk maps serve multiple purposes. They offer real-time information to emergency response commanders regarding oil spill hazard prediction, and they also enable the effective development of emergency response strategies and disposal plans for potential oil spill incidents. This is achieved by generating risk maps for various scenarios using the approach outlined in the first component. Additionally, these maps assist in the assessment and planning of resource allocation for oil containment.

A flood Impact-Based forecasting system by fuzzy inference techniques

The increasing frequency of severe floods worldwide highlights the need for effective communication and dissemination of information about potential floods and their possible consequences. Modern technology, such as social media and early warning systems, can play a crucial role in informing the public about impending floods and their potential impact. Impact-Based Forecasting (IBF), conveying meteorological and hydrological data, has emerged as a promising approach to inform vulnerable communities about the potential impact of flood disasters and to mitigate associated risks for resilience enhancement and losses minimization. This study proposes a flood impact-based forecasting system (FIBF) that uses flexible fuzzy inference techniques to construct a relation matrix of likelihoods and impacts and estimate the level of risk and impact for inducing storms. We construct a three-layer fuzzy inference model to determine the flood impact based on the combined effects of hazard, exposure, and vulnerability and then establish a FIBF warning product (up to 36 h ahead) to timely address flood information for inundation-prone regions of the Kemaman River basin in Malaysia. The FIBF can be displayed using Google Maps, providing a spatial visualization of the flooding situation to help decision-makers better understand the extent and severity of the flooding, make informed decisions on emergency response and resource allocation, and take proactive measures to reduce the negative impacts of flooding. Thus, the developed FIBF can be especially helpful in mitigating the socioeconomic costs of flood hazards.

Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China

The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) has become a focus of research in the post-epidemic era. Based on China’s experience in preventing and controlling COVID-19, this paper first analyzes the characteristics and process of ERA in public health emergencies, and then synthesizes the relevant Chinese studies in recent years to identify the intelligent technologies affecting ERA in China using word frequency analysis technology. We also construct an intelligent emergency resource allocation mechanism in four areas: medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence. Finally, we use the entropy-TOPSIS method to evaluate the impact of intelligent technologies on ERA, and we rank the criticality of intelligent technologies. The experimental results show that (i.) medical intelligence and management intelligence are the keys to developing intelligent ERA, and (ii.) among the identified essential intelligent technologies, artificial intelligence (AI), and big data technology have a more significant and critical role in emergency resource intelligence allocation.

Distributionally robust chance-constrained programming for multi-period emergency resource allocation and vehicle routing in disaster response operations

Emergency resource allocation and vehicle routing are the most essential and inseparable response actions in emergency management after disasters. In particular, disaster response operations are significantly affected by high uncertainty and incomplete dynamic information of demand and risk. For this purpose, we construct a risk-based ambiguity set for modeling the distributional uncertainty in the demand and describing the coupling relationship between the demand and risk in different periods (e.g., secondary disasters strike). Against this background, we present two distributionally robust chance-constrained programming (DRCCP) models with both individual and joint chance constraints for multi-period emergency resource allocation and vehicle routing problem under demand distributional ambiguity. For DRCCP with individual chance constraint, we can derive the computationally tractable reformulation of the proposed model with a safe approximation index. For DRCCP with joint chance constraint, we can use Bonferroni’s approximation to obtain a set of tractable individual chance constraints. As for the solution method, we first decompose the original model into the emergency resource allocation and vehicle routing subproblems, and then develop an efficient adaptive large neighborhood search (ALNS) algorithm. We evaluate the performance of the proposed ALNS heuristic algorithm on a hypothetical instance set and show that the ALNS algorithm is capable of producing high-quality solutions within a reasonable computing time. We also conduct a real case study of the Wenchuan earthquake in China to demonstrate the superiority of the proposed DRCCP approach in a comparative perspective. In addition, we provide some possible extensions of the considered problem. Finally, we explore the managerial insights that may be useful for the disaster response operation.

Characteristics of eye-related emergency visits and triage differences by nurses and ophthalmologists: Perspective from a single eye center in southern China.

To describe characteristics of eye-related emergency department (ED) visits and investigate differences in priorities assigned to patients by triage nurses and ophthalmologists. A prospective survey was conducted at the ED of Zhongshan Ophthalmic Center from January 1, 2021, to May 31, 2021. Clinical data from patients with acute ophthalmic conditions lasting less than 7 days were collected via a standard questionnaire and the urgency levels assigned by nurses and physicians were also recorded. Binary logistic regression was performed to identify characteristics associated with truly emergency conditions and up- or down-triage. A total of 1907 patients were enrolled, with 582 (30.5%) classified as "non-emergency." Red eye (69.7%), eye pain (53.0%), ocular trauma (44.1%), tearing (43.6%), and blurred vision (43.1%) were the most common complaints. Truly emergency tended to be male (OR 2.019, p < 0.001) and with unilateral eye involvement (OR 2.992, p < 0.001). Nurses prioritized conjunctival, scleral, closed ocular trauma and eyelid diseases over doctors while giving less priority to open ocular trauma, cornea, uveitis, and vitreoretinal diseases (p < 0.05). Overemphasis on mild blurred vision (OR 3.718, p = 0.001) and insufficient understanding of conjunctival diseases without red eye (OR 0.254, p = 0.001) were associated with conjunctival disease "up-triage." Insufficient awareness of moderate and severe blurred vision was associated with "down-triage" for ocular trauma (OR 3.475, p = 0.001 and OR 2.422, p = 0.020, respectively). Ophthalmic EDs are typically flooded with patients suffering from acute ocular problems, with a considerable portion for non-emergency conditions. The identification of characteristics associated with truly emergency cases and nurses' triage preferences is valuable in providing target guidance for future ED practice and facilitating the proper allocation of emergency resources.

The Ethics of Implementing Emergency Resource Allocation Protocols.

AbstractWe explore the various ethical challenges that arise during the practical implementation of an emergency resource allocation protocol. We argue that to implement an allocation plan in a crisis, a hospital system must complete five tasks: (1) formulate a set of general principles for allocation, (2) apply those principles to the disease at hand to create a concrete protocol, (3) collect the data required to apply the protocol, (4) construct a system to implement triage decisions with those data, and (5) create a system for managing the consequences of implementing the protocol, including the effects on those who must carry out the plan, the medical staff, and the general public. Here we illustrate the complexities of each task and provide tentative solutions, by describing the experiences of the Coronavirus Ethics Response Group, an interdisciplinary team formed to address the ethical issues in pandemic resource planning at the University of Rochester Medical Center. While the plan was never put into operation, the process of preparing for emergency implementation exposed ethical issues that require attention.

Optimising the Distribution of Multi-Cycle Emergency Supplies after a Disaster

In order to achieve rapid and fair distribution of emergency supplies after a large-scale sudden disaster, this paper constructs a comprehensive time perception satisfaction function and a comprehensive material loss pain function to portray the perceived satisfaction of disaster victims based on objective constraints such as limited transport, multimodal transport and supply being less than demand, and at the same time considers the subjective perception of time and material quantity of disaster victims under limited rational conditions, and constructs a multi-objective optimisation model for the dispatch of multi-cycle emergency supplies by combining comprehensive rescue cost information. For the characteristics of the proposed model, based on the NSGA-II algorithm, generalized reverse learning strategy, coding repair strategy, improved adaptive crossover, variation strategy, and elite retention strategy are introduced. Based on this, we use the real data of the 2008 Wenchuan earthquake combined with simulated data to design corresponding cases for validation and comparison with the basic NSGA-II algorithm, SPEA-II and MOPSO algorithms. The results show that the proposed model and algorithm can effectively solve the large-scale post-disaster emergency resource allocation problem, and the improved NSGA- II algorithm has better performance.