Disaster Relief Operations Research Articles

Optimization of emergency material distribution routes in flood disaster with truck‐speedboat‐drone coordination

AbstractTo improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.

Resource management in disaster relief: a bibliometric and content-analysis-based literature review

AbstractDisasters cause huge economic losses, affect the lives of many people, and severely damage the environment. Effective resource management during disaster preparedness and response phases improves distribution efforts and service levels and, hence, accelerates the disaster relief operations. Resource management in response to catastrophe has received increasing research attention in recent years, but no review paper focuses on this specific topic. Thus, the main purpose of this paper is to review the existing literature on resource management for disaster relief published in English in peer-reviewed journals in order to fill the gap. We apply bibliometric, network, and content analyses in our review to identify popular research topics, classify the literature into research clusters, and analyze the interrelationships between these research clusters. The second purpose of this paper is to identify gaps and trends in existing research. Finally, we propose six future research directions that provide a roadmap for resource management research for disaster relief.

Distributed Ledger-Based Authentication and Authorization of IoT Devices in Federated Environments

One of the main security challenges when federating separate Internet of Things (IoT) administrative domains is effective Identity and Access Management, which is required to establish trust and secure communication between federated IoT devices. The primary goal of the work is to develop a “lightweight” protocol to enable authentication and authorization of IoT devices in federated environments and ensure the secure communication of IoT devices. We propose a novel Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI) which takes advantage of the unique fingerprint of IoT devices based on their configuration and additional hardware modules, such as Physical Unclonable Function, to provide flexible authentication and authorization based on Distributed Ledger technology. Moreover, LAAFFI supports IoT devices with limited computing resources and devices not equipped with secure storage space. We implemented a prototype of LAAFFI and evaluated its performance in the Hyperledger Fabric-based IoT framework. Three main metrics were evaluated: latency, throughput (number of operations or transactions per second), and network resource utilization rate (transmission overhead introduced by the LAAFFI protocol). The performance tests conducted confirmed the high efficiency and suitability of the protocol for federated IoT environments. Also, all LAAFFI components are scalable as confirmed by tests. We formally evaluated LAAFFI security using Verifpal as a formal verification tool. Based on the models developed for Verifpal, we validated their security properties, such as message secrecy, authenticity, and freshness. Our results show that the proposed solution can improve the security of federated IoT environments while providing zero-day interoperability and high scalability. Compared to existing solutions, LAAFFI is more efficient due to the use of symmetric cryptography and algorithms adapted for operations involving IoT devices. LAAFFI supports multiple authorization mechanisms, and since it also offers authentication and accountability, it meets the requirements of Authentication, Authorization and Accounting (AAA). It uses Distributed Ledger (DL) and smart contracts to ensure that the request complies with the policies agreed between the organizations. LAAFFI offers authentication of devices belonging to a single organization and different organizations, with the assurance that the encryption key will be shared with another device only if the appropriate security policy is met. The proposed protocol is particularly useful for ensuring the security of federated IoT environments created ad hoc for special missions, e.g., operations conducted by NATO countries and disaster relief operations Humanitarian Assistance and Disaster Relief (HADR) involving military forces and civilian services, where immediate interoperability is required.

Fire management in the disaster prevention and mitigation division of lad luang municipality, phra pradaeng district, samut prakan province

This article aims to study the management of problems, obstacles, and suggest guidelines for the development of fire management in Lat Luang Municipality's public disaster prevention and relief work. Phra Pradaeng District Samut Prakan Province, sample group used in this study The total population was determined to be a sample of 50 people and a sample of 15 people was used to collect qualitative data. The researcher used quantitative and qualitative research processes. and collected data using questionnaires and in-depth interviews. The results of the research found that 1) Fire management of disaster prevention and relief work in Lat Luang Municipality. Phra Pradaeng District Samut Prakan Province as a whole is at a good level. 2) Guidelines for developing fire management of disaster prevention and relief work in Lat Luang Municipality. Phra Pradaeng District Samut Prakan Province: There should be a situation commander with specific knowledge and expertise in direct disaster prevention and relief operations. Including creating awareness for the people to be aware and participate in fire prevention in the community. Promote participation and integration of all sectors in management, including the public and private sectors, the public, volunteer foundations and training centers. And there should be sufficient budget support for all sectors to participate in sustainable disaster prevention and relief.

A fuzzy multi-objective programming model for the delivery and distribution of humanitarian relief materials

Disaster relief presents many unique logistics challenges, including damaged transportation infrastructure, multiple conflicting goals, and secondary disasters caused by spontaneous disaster relief. The delivery and distribution of humanitarian relief materials faced by nonprofit organizations (NPOs) poses a critical challenge due to the highly unpredictable nature of disasters, conflicting missions, and the need to navigate government regulations in disaster management. In this paper, we propose a novel multi-objective disaster relief modeling system for NPOs, considering (1) prioritization (providing medical services to seriously injured victims), (2) effectiveness (degree of satisfaction), (3) efficiency (operational costs), and (4) equity (cost of maintaining order). To incorporate the practical constraints of disaster relief operations, we include factors such as road damage, transportation capacity limitations, and route flow saturation as part of the model formulation. We then utilize fuzzy multi-objective programming to optimize the proposed disaster relief model, considering two relief distribution schemes: a common NPOs' spontaneous relief scheme and a newly designed traffic control scheme with government regulation. Based on the real-world scenario of the 2008 Wenchuan earthquake, we demonstrate that the designed traffic control scheme exhibited remarkable improvement among the NPOs' four objectives compared to the spontaneous relief scheme. Moreover, in the process of solving fuzzy multi-objective programming problems, artificial intelligence technology is employed to handle the fuzziness in multi-objective and automatically adjust parameters, making the solution results better and in line with practical situations. And we also validate the proposed model can be solved in polynomial time, meeting the engineering practice requirements, and can be easily extend to most disaster relief distribution scenarios.

Geographic Information System for Shortest Route Search for Clinics in Pamekasan Regency Using the Djikstra Method

Earthquake natural disasters can cause significant infrastructure damage, hindering community access and mobility. In such a situation, determining the shortest path for evacuation becomes very important. This research uses linear programming by utilising Excel Solver to determine the shortest evacuation route for earthquake natural disasters. Excel Solver is a feature in Microsoft Excel that serves as an analytical tool to solve linear optimisation problems. Excel solver can be used to find solutions that maximise or minimise the objective function, with respect to predetermined constraints. This research utilises relevant earthquake natural disaster simulation data as constraints in the optimisation model. The results show that the model can generate shortest paths that fulfil various constraints and criteria. The model can also be used to predict the time required to reach the destination. This research makes an important contribution to the development of tools and methods for shortest path optimisation in natural disaster scenarios. The optimisation model developed in this research can be used to assist decision makers in improving the efficiency and effectiveness of evacuation and disaster relief operations.

Climate Refugees in India: Seeking Security between Disaster Diplomacy and Strategic Ambiguity

Abstract Among the 100 million refugees and displaced persons in 2022, the category of “climate refugees” has become more salient, yet countries still do not know how to handle it. Two aspects of climate refugees also remain understudied; how climate refugees are perceived, since they are viewed through the dual lenses of climate risks and migratory flows and how these perceptions impact policies. Climate refugees are thus doubly impacted by the spill-over effect of securitization processes in the fields of climate change and immigration. This paper analyzes how climate- and migration-security legislation in a resource-constrained nation conceptualizes climate refugees, and how their diverse conceptual categories spill over into policymaking and create a mutually beneficial and humanitarian approach for host and migrant populations. This paper uses India as a case study based on its historical practice of refugee protection despite significant resource constraints, high risk of inbound climate refugees, participation in key global agreements, and domestic discourse over climate and immigration securitization. Using policy analysis and expert interviews, this study demonstrates that India successfully exemplifies a broadly humanitarian climate mobility regime in the South Asian region through relocations, Humanitarian Assistance and Disaster Relief operations, and ad hoc legal protection. Despite crucial structural limitations, India illustrates a democratic global south template implicitly recognizing migrants’ vulnerability to climate change and attempts to minimize risk, with potential for replication in other developing and developed nations. This normative policy framework, notwithstanding its limitations, presents an alternative to the threat-centric and unsustainable securitization of climate migration practiced in the Global North.

Optimisation of Evacuation Route Determination in an Earthquake Natural Disaster Scenario Using an Excel Solver

Earthquake natural disasters can cause significant infrastructure damage, hindering community access and mobility. In such a situation, determining the shortest path for evacuation becomes very important. This research uses linear programming by utilising Excel Solver to determine the shortest evacuation route for earthquake natural disasters. Excel Solver is a feature in Microsoft Excel that serves as an analytical tool to solve linear optimisation problems. Excel solver can be used to find solutions that maximise or minimise the objective function, with respect to predetermined constraints. This research utilises relevant earthquake natural disaster simulation data as constraints in the optimisation model. The results show that the model can generate shortest paths that fulfil various constraints and criteria. The model can also be used to predict the time required to reach the destination. This research makes an important contribution to the development of tools and methods for shortest path optimisation in natural disaster scenarios. The optimisation model developed in this research can be used to assist decision makers in improving the efficiency and effectiveness of evacuation and disaster relief operations.

Crawling, climbing, perching, and flying by FiBa soft robots.

This paper introduces an approach to fabricating lightweight, untethered soft robots capable of diverse biomimetic locomotion. Untethering soft robotics from electrical or pneumatic power remains one of the prominent challenges within the field. The development of functional untethered soft robotic systems hinges heavily on mitigating their weight; however, the conventional weight of pneumatic network actuators (pneu-nets) in soft robots has hindered untethered operations. To address this challenge, we developed film-balloon (FiBa) modules that drastically reduced the weight of soft actuators. FiBa modules combine transversely curved polymer thin films and three-dimensionally printed pneumatic balloons to achieve varied locomotion modes. These lightweight FiBa modules serve as building blocks to create untethered soft robots mimicking natural movement strategies. These modules substantially reduce overall robot weight, allowing the integration of components such as pumps, valves, batteries, and control boards, thereby enabling untethered operation. FiBa modules integrated with electronic components demonstrated four bioinspired modes of locomotion, including turtle-inspired crawling, inchworm-inspired climbing, bat-inspired perching, and ladybug-inspired flying. Overall, our study offers an alternative tool for designing and customizing lightweight, untethered soft robots with advanced functionalities. The reduction of the weight of soft robots enabled by our approach opens doors to a wide range of applications, including disaster relief, space exploration, remote sensing, and search and rescue operations, where lightweight, untethered soft robotic systems are essential.

A Deep Dive into The Challenges of Disaster Relief Operations

A Deep Dive into The Challenges of Disaster Relief Operations

A Mathematical Model for Integrated Disaster Relief Operations in Early-Stage Flood Scenarios

When a flood strikes, the two most critical tasks are evacuation and relief distribution. It is essential to integrate these tasks, particularly before the floodwater reaches the vulnerable area, to minimize loss and damage. This paper presents a mathematical model of vehicle routing problems to optimize an integrated disaster relief operation. The model addresses routing for both the evacuation and relief distribution tasks in the early stages of a flood, aiming to identify a minimal number of vehicles required with their corresponding routes to transport vulnerable individuals and simultaneously distribute emergency relief. The new model incorporates several features, including vehicle reuse, multi-trip and split delivery scenarios for evacuees and emergency relief items, uncertainty in evacuation demands, and closing time windows at evacuation points. Due to the complexity of vehicle routing problems, particularly in large-scale scenarios, the exact approach for obtaining optimal solutions is time-consuming. Therefore, we propose the use of a metaheuristic algorithm, specifically a modified genetic algorithm, to find an approximate solution for the proposed model. We apply the developed model and modified algorithm to various simulated flood scenarios and a real-life case study from Indonesia. The experimental results demonstrate that our approach requires fewer vehicles compared to standard models for similar scenarios. Moreover, while the exact approach fails to find optimal solutions within a reasonable timeframe for large-scale scenarios, our new approach provides near-optimal solutions in a much shorter time. In smaller simulated scenarios, the modified genetic algorithm obtains optimal or near-optimal solutions approximately 92.5% faster than the exact approach.

Al Based Automatic alarm Generation and Dropping of Payload at a Particular Object through a Drone

Unmanned aerial vehicles (UAVs), commonly referred to as drones, are becoming more and more prevalent in a number of industries, including logistics, surveillance, and reconnaissance. This work introduces a novel framework that automatically generates alerts and precisely targets specific points of interest for UAV payload delivery using artificial intelligence (AI) algorithms. Our framework seamlessly integrates real-time object detection and recognition capabilities with an advanced AI-driven decision-making module to enable autonomous functionality. The operational process starts with using on board sensors and cameras to continuously monitor the environment. The AI algorithms carefully consider all the options when they identify an object of interest, such as a suspicious package or a predetermined target. They then determine the optimal course of action. If necessary, the system triggers an alert and arranges for a series of actions to enable the UAV to drop a payload at the designated location. Payload delivery mechanisms, advanced object detection algorithms, path planning algorithms that optimize UAV navigation, and deep learning models tailored for object recognition are some of the framework's key elements. The AI-powered decision-making module ensures that choices are made accurately, promptly, and with consideration for the mission's goals, the law, and the environment. A comprehensive evaluation of the proposed framework's efficacy is conducted through simulated and real-world experiments, showcasing its versatility in various contexts. The results Validate the framework's remarkable accuracy in object detection and recognition, as well as its reliable payload delivery capabilities. Furthermore, the system's autonomy lessens the need for human intervention, which makes it particularly suitable for applications that demand accuracy and speed. Our work advances the development of AI-powered unmanned aerial vehicles (UAVs) designed for automated monitoring, law enforcement, and disaster relief operations. With its great potential to improve situational awareness and operational effectiveness in a variety of environments, the proposed framework could usher in a new era of autonomous aerial system advancements.

Fuzzy best–worst method–based approach for warehouse location selection and a case study in Izmir

PurposeThe study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.Design/methodology/approachThe paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.FindingsIt is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.Originality/valueThis study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

Rapid Crisis Response in Resilient Cities: Emergency Response to and Reconnaissance of Earthquake Disasters in Taiwan

The devastating Chichi earthquake in Taiwan in 1999 highlighted the importance of disaster prevention and relief operations. In response to the challenge of extreme events and compound disasters, the National Science and Technology Center for Disaster Reduction (NCDR) contributes to operations at the Central Emergency Operation Center (CEOC) of Taiwan by providing integrated information and timely suggestions, and it delivers a common operational picture to central and local governments through a decision support system. This study was undertaken to explore the emergency response towards the reconnaissance of earthquake disasters in Taiwan to illustrate how resilient cities ensure rapid crisis response with a focus on the NCDR disaster response decision support system. This study examines the decision support system used following the 2016 ML 6.6 Meinong earthquake. The results demonstrate that the first line of relief for the emergency response commander is data-driven decision-making.

Design and management of humanitarian supply chains for pandemics: lessons from COVID-19

The COVID-19 pandemic has been an unprecedented challenge for humanity, causing widespread disruptions to daily life and economies worldwide. However, amidst the challenges, it has also presented an opportunity to reflect on our competencies, capabilities, and objectives. One area that has gained significant attention during this time is the humanitarian supply chain management theories, which offered significant directions to the scholars engaged in operations and supply chain management, practitioners and policymakers. To further advance the theoretical boundaries of the humanitarian supply chain and expand the scope and boundaries of the discipline, a special issue has been organised. This issue includes 24 carefully selected articles that have gone through multiple rounds of review. The articles are all centred on the humanitarian crisis resulting from COVID-19 and offer a comprehensive understanding of the challenges faced by various stakeholders. Additionally, one relevant article from a previous issue has been included, which explores the potential use of drones in disaster relief operations. The articles included in this special issue have significant implications for theory, practice, and policy, providing valuable insights and direction for further research to expand the scope of the discipline. This particular issue is a crucial contribution to humanitarian supply chain management. It provides a deeper understanding of the complex challenges faced during a crisis and the best practices to overcome them.

A First Responder to the World

In the twenty-first century, providing humanitarian assistance to other nations forms an essential part of the U.S. military's mission. Designed to “relieve or reduce endemic conditions such as human suffering, disease, hunger, [and] privation,” the military's humanitarian tasks include such activities as assisting refugees, delivering food and medical equipment to partner nations, and removing unexploded landmines.1 But among the most recognized—and best publicized—of the U.S. Armed Forces’ humanitarian activities are its foreign disaster relief operations. In response to such recent catastrophes as the 2010 earthquake in Haiti, the 2011 tsunami in Japan, 2019's Cyclone Idai in Mozambique, and the 2023 earthquakes in Turkey, the Department of Defense launched extensive humanitarian operations. Taking advantage of the U.S. military's vast global footprint, military leaders dispatched ships and aircraft, service personnel, relief supplies, equipment, and other material and logistical aid to the scene of these crises and to many others like them globally.2 Carried out for the humanitarian goals of saving lives and reducing suffering, each of these foreign disaster aid operations also shared a critical diplomatic objective: “generating goodwill” for the United States and creating a “favorable impression” of its armed forces.3 Never purely altruistic, the U.S. military's responses to global disasters also function as a valuable instrument of contemporary U.S. foreign policy.

Individually optimized estimation of energy expenditure in rescue workers using a tri-axial accelerometer and heart rate monitor.

Objectives: This study aimed to provide an improved energy expenditure estimation for heavy-load physical labor using accelerometer data and heart rate (HR) measured by wearables and to support food preparation and supply management for disaster relief and rescue operations as an expedition team. Methods: To achieve an individually optimized estimation for energy expenditure, a model equation parameter was determined based on the measurements of physical activity and HR during simulated rescue operations. The metabolic equivalent of task (MET), which was measured by using a tri-axial accelerometer and individual HR, was used, where two (minimum and maximum) or three (minimum, intermediate, and maximum) representative reference points were selected for each individual model fitting. In demonstrating the applicability of our approach in a realistic situation, accelerometer-based METs and HR of 30 males were measured using the tri-axial accelerometer and wearable HR during simulated rescue operations over 2days. Results: Data sets of 27 rescue operations (age:34.2 ± 7.5years; body mass index (BMI):22.9 ± 1.5kg/m2) were used for the energy expenditure estimation after excluding three rescue workers due to their activity type and insufficient HR measurement. Using the combined approach with a tri-axial accelerometer and HR, the total energy expenditure increased by 143% for two points and 133% for three points, compared with the estimated total energy expenditure using only the accelerometer-based method. Conclusion: The use of wearables provided a reasonable estimation of energy expenditure for physical workers with heavy equipment. The application of our approach to disaster relief and rescue operations can provide important insights into nutrition and healthcare management.

Supporting disaster relief operations through RFID: enabling visibility and coordination

PurposeIn the aftermath of disasters, humanitarian actors need to coordinate their activities based on accurate information about the disaster site, its surrounding environment, the victims and survivors and the supply of and demand for relief supplies. In this study, the authors examine the characteristics of radio frequency identification (RFID) technology and those of disaster relief operations to achieve information visibility and actor coordination for effective and efficient humanitarian relief operations.Design/methodology/approachBuilding on the contingent resource-based view (CRBV), the authors present a model of task-technology fit (TTF) that explains how the use of RFID can improve visibility and coordination. Survey data were collected from humanitarian practitioners in India, and partial least squares (PLS) analysis was used to analyze the model.FindingsThe characteristics of both RFID technology and disaster relief operations significantly influence TTF, and TTF predicts RFID usage in disaster relief operations, providing visibility and coordination. TTF is also a mediator between the characteristics of RFID technology and disaster relief operations and between visibility and coordination.Social implicationsThe many recent humanitarian disasters have demonstrated the critical importance of effective and efficient humanitarian supply chain and logistics strategies and operations in assisting disaster-affected populations. The active and appropriate use of technology, including RFID, can help make disaster response more effective and efficient.Originality/valueHumanitarian actors value RFID technology because of its ability to improve the visibility and coordination of relief operations. This study brings a new perspective to the benefits of RFID technology and sheds light on its antecedents. The study thus expands the understanding of technology in humanitarian operations.

From Challenges to Solutions: Stakeholder Roles in Disaster Relief Operations

Disaster relief operations (DROs) in Malaysia require the coordinated efforts of various stakeholders to mitigate impacts and enhance community resilience. However, communication barriers, lack of coordination, and shortage of resources often present challenges that can hinder the efficiency and effectiveness of relief efforts. Thus, this study aimed to gain an in-depth understanding of how key stakeholders in Malaysia navigate inter-organizational difficulties during DROs. Specifically, the objective was to identify the key challenges faced and explore initiatives implemented by stakeholders to navigate these challenges to strengthen relief operations. To address these objectives, in-depth interviews were conducted with seven experienced DRO stakeholders across three disaster-prone states in Malaysia: Pahang, Selangor, and Kelantan. Data collection involved using notebooks, video recorders, and voice recorders, and the transcribed data was analyzed using Atlas.ti software for thematic analysis. The analysis revealed four main themes: communication, coordination, manpower, and equipment. The stakeholders proactively strengthened relief operations by creating training programs for disaster response teams, providing comprehensive disaster-related information to the community, developing a sensor-based early warning system, and launching a community engagement program called Kampung Siaga 221 to raise disaster awareness. These initiatives reflect a strategic approach aimed at building more resilient communities, ultimately reducing challenges faced during DROs. The findings of this study contribute to the existing literature on stakeholder involvement in disaster management and offer valuable insights that can inform the development of more coordinated and holistic strategies for DROs in Malaysia. Future research could examine the long-term impact and scalability of these initiatives to other regions, as well as investigate the perspectives of disaster-affected communities to further improve DROs.

Hospital professional staff awareness of hospital plans for the provision of teams for medical support in case of disasters

Background/Aim. Resuscitation and surgical medical aid are the most important medical procedures in disaster re-lief operations. The limited time frame available for life-saving activities in such circumstances demands that resuscitation and surgical medical teams be established and trained for timely reaction and disaster medical support prior to disaster occurrence. The aim of the study was to analyze the awareness among the hospital medical staff in the Plovdiv Region of the hospital response plan in case of disaster, which concerns surgical and resuscitation team composition and tasks for disaster medical support. Methods. An anonymous survey, consisting of 55 questions about hospital staff awareness of the medical activities described in the hospital disaster medical support plan, was conducted between July and September 2019 among 295 hospital medical professionals in the Plovdiv Region, Bulgaria. Descriptive statistics and Pearson?s ?2 test were used in the statistical analysis of results. Results. The survey showed that the majority of medical staff demonstrated complete ignorance regarding the presence, number, and tasks of the specialized medical teams planned for hospital disaster response. Employees of multidisciplinary hospitals for active treatment were more familiar with the aforementioned plans than their colleagues from the university multidisciplinary hospitals. Conclusion. Insufficient awareness about human resources foreseen for disaster medical support negatively impacts the hospital surge capacity, leading to inadequate medical assistance in such cases. Hospital tactical disaster preparedness must be ameliorated by conducting a more rigorous training program among medical staff related to the planned hospital activities in case of disastrous events, especially for those working in emergency departments, intensive care units, and surgical departments.