Stochastic Programming Approaches to Logistics and Resource Optimization in Hurricane, Earthquake, and Flood Disaster Response

The Hawkesbury-Nepean Valley, Australia’s longest coastal catchment, is spanned by a river system of more than 470 km, that runs from Goulburn to Broken Bay, covering a total area of over 2.2 million hectares. This region has remained prone to flood events, with considerable mortalities, economic impacts and infrastructural losses occurring quite regularly. The topography, naturally variable climatic conditions and the ‘bathtub’ effect in the region are responsible for the frequent flood events. In response, the Government at the national/federal, state and local level has focused on the design of efficient flood risk management strategies with appropriate evacuation plans for vulnerable communities from hospitals, schools, childcare and aged care facilities during a flood event. Despite these overarching plans, specialized response and evacuation plans for aged care facilities are critical to reducing the loss incurred by flood events in the region. This is the focus of this present paper, which reviews the history of flood events and responses to them, before examining the utilization of artificial intelligence (AI) techniques during flood events to overcome the flood risks. An early flood warning system, based on AI/Machine Learning (ML) strategy is being suggested for a timely decision, enhanced disaster prediction, assessment and response necessary to overcome the flood risks associated with aged care facilities within the Hawkesbury-Nepean region. A framework entailing AI/ML methods for identifying the safest route to the destination using UAV and path planning has been proposed for timely disaster response and evacuation of the residents of aged care facilities.

Cloud computing has emerged as a transformative force for nonprofit organizations and humanitarian efforts worldwide, enabling them to optimize operations, enhance decision-making, and scale their impact despite resource constraints. This comprehensive article examines how cloud technologies are revolutionizing the nonprofit sector across multiple dimensions: operational efficiency, data-driven decision-making, global collaboration, resource optimization, and scalable interventions. Through detailed case studies spanning diverse sectors, including healthcare, education, agriculture, disaster response, and environmental conservation, the article demonstrates how cloud adoption is helping organizations redirect resources from administrative overhead to mission-critical activities. The evaluation highlights how cloud-based solutions facilitate seamless global collaboration, enable sophisticated analytics for program improvement, optimize resource allocation, and provide unprecedented scalability to address global challenges. Organizations implementing these technologies consistently report significant improvements in service delivery capabilities, operational efficiency, and overall mission impact, establishing cloud transformation as a strategic imperative for nonprofits seeking to maximize their social impact in an increasingly complex global environment.

Los programas de asistencia social tienen como propósito brindar apoyo a la población vulnerable o en condiciones de pobreza, algunos de ellos a través de la producción y distribución directa de alimentos. Dada su integración con la población vulnerable, sus capacidades para movilizar carga, la amplia cobertura geográfica y sobre todo su orientación social, dichos programas podrían ser adaptados y preparados para servir como sistema de apoyo ante eventos disruptivos como los desastres naturales. Sin embargo, dicha adaptación hasta ahora no ha sido mencionada en la literatura. Este artículo propone la adaptación de un programa de asistencia social llamado Bienestarina, para la atención de un riesgo de terremoto en la Ciudad de Bogotá-Colombia. Para ello se implementó una metodología de 5 fases, con la cual se identificaron los elementos constitutivos de la cadena de suministro, se determinó un escenario potencial de riesgo de terremoto y el posible efecto del mismo sobre las capacidades de la red. La información obtenida fue el soporte para el diseño de los modelos matemáticos propuestos, a través de los cuales se evaluó un escenario de riesgo con variaciones en los parámetros de capacidades y demandas de asistencia humanitaria, obteniendo así un marco de resultados que permiten entrever la bondad de la integración de este programa en la respuesta a desastres. El aporte principal de la investigación es la evaluación de la integración de las capacidades de los programas de asistencia social para la respuesta a escenarios de desastre, y de manera accesoria la formulación de modelos matemáticos para la planificación de la respuesta considerando la vulnerabilidad de la infraestructura y el uso de concepto de “Zonas seguras” para la localización de instalaciones.

Many studies have used Advanced Land Observing Satellite 2 (ALOS-2) synthetic aperture radar (SAR) interferograms to make remarkable advances toward understanding and assessing seismic hazards. Next-generation satellites will make abundant L-band SAR data available in the near future, enabling even more progress in earthquake research and disaster response. Because a deep understanding of the performance capabilities and limitations of the only existing L-band satellite, ALOS-2, is crucial for planning future L-band SAR missions, this study produced SAR interferograms using ALOS-2 data for large global earthquakes that occurred between August 2014 and December 2016. All of the interferograms produced (49 from 30 seismic events) exhibited adequate coherence even in densely vegetated areas, where C-band interferograms tend to be unreliable because of severe decorrelation. Interferograms for 23 of the 30 seismic events successfully captured significant coseismic deformation signals. These results indicated that ALOS-2 can be leveraged to detect deformations with a high spatial resolution and a high precision unavailable from other instruments, particularly in tropical areas. In addition to the high coherence and high spatial resolution, ALOS-2 features such as left-looking and rapid emergency observations are also advantageous for earthquake research and disaster response. Although current baseline conditions are not always desirable for ALOS-2 interferograms because of limited observation resources, planned L-band missions (e.g., ALOS-4 and NASA-ISRO SAR), which will offer much wider coverage and higher observation frequency, are expected to improve this situation.

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

As one of the most important communication scenarios in the coming fifth generation (5G) cellular networks, ultrareliable and low-latency communication (URLLC) is promising to enable real-time wireless control systems. However, one of the biggest challenges is that how to integrate URLLC and control performance together to maximize the overall system performance. In this paper, we investigate the resource allocation for URLLC uplink in real-time wireless control systems. Specifically, we first discuss the relationship between communication and control performance. Based on that, we convert the hybrid co-design problem into a regular wireless resource allocation problem. Then, we propose an iteration algorithm to obtain the optimal wireless resource allocation. Simulation results indicate the performance of our method.

Optimal resource allocation across related channels

Cooperative dynamic free-space optical (FSO) networks exploit the fact that atmospheric losses are distance dependent to enhance the performance of FSO networks. This enhancement is achieved by sharing the resources of shorter links among different nodes in the network. In this paper, two joint transceiver placement and resource allocation schemes are proposed to optimally place FSO redundant transceivers based on optimal resource allocation in cooperative dynamic FSO networks. Specifically, one scheme increases reliability and capacity, while the other increases reliability and fairness of cooperative dynamic FSO networks during severe weather conditions. The schemes are formulated as multi-objective and bi-level integer linear programming problems and solved using an exhaustive search to obtain optimal solutions. The numerical results reveal that higher reliabilities can be achieved with enhanced capacities and fairness using the first and second schemes, respectively. Furthermore, these improvements are achieved by using many fewer numbers of FSO redundant transceivers than those of random placement.

The increasing frequency of high-impact, low-probability (HILP) events, such as natural disasters and cyberattacks, poses significant risks to the energy sector, highlighting the urgent need for resilient distribution grids. This paper proposes a novel framework to enhance power grid resilience following HILP events by strategically deploying mobile battery energy storage systems (MBESSs) and dynamically forming microgrids. This approach addresses the shortcomings of strategies that often focus on individual resilience aspects. The framework leverages both pre-event optimization and post-event dynamic resource allocation to achieve robust and efficient grid recovery. A novel quantitative metric is developed to evaluate grid resilience, enabling informed decision-making for MBESS deployment and scheduling. Before HILP events occur, the framework optimizes MBESS charge/discharge schedules to improve operational performance and enhance grid resilience. Following an event, the framework rapidly forms microgrids and dynamically relocates MBESSs to prioritize the restoration of loads, accelerating recovery efforts. By combining MBESS capabilities with microgrid formation and optimized resource allocation, this work provides a comprehensive and adaptable framework for bolstering grid resilience in the face of unforeseen disruptions, ultimately contributing to a more resilient and reliable power grid.

Natural and artificial disasters present significant societal challenges, requiring effective prediction and resource allocation strategies. Integrating disaster prediction and resource optimization with machine learning approaches, the study will improve disaster management systems and apply Random Forest, XGBoost, and Neural Networks as models alongside optimization methods such as linear programming and reinforcement learning. Key findings include precision regional outcome severity estimates and optimized resource allocation strategies for earthquakes, flooding, and similar scenarios. The results show increased speed and efficiency in disaster response, with practical implications for preparation and mitigation.

Short-lived extreme rainfall events pose major challenges for flood forecasting and disaster response because of their high intensity and strong spatial localization. This study examines how rainfall datasets with different spatial and temporal resolutions represent such events over India. A total of 100 extreme rainfall cases are analysed using two datasets: a high-resolution satellite-based precipitation dataset (8 km, hourly) that combines multi-sensor satellite observations calibrated with rain gauge measurements, and the daily gridded rainfall dataset from the India Meteorological Department (IMD) at 25 km resolution derived from a dense gauge network. To isolate the effects of data aggregation, the high-resolution dataset is also converted to daily totals and spatially coarsened to 25 km resolution. The original hourly dataset is treated as the reference. Statistical comparisons based on peak intensity, time-series behaviour, and intensity-weighted visualization show that IMD daily data underestimate peak rainfall by up to 55.5%, while temporally aggregated and spatially coarsened datasets underestimate peaks by about 22.7% and 39.5%, respectively. These biases are most pronounced in convectively active and urban regions where rainfall can exceed 80 mm h⁻¹ within 1–2 hours. The results highlight the importance of high-resolution hourly data for capturing short-duration rainfall extremes and improving real-time flood forecasting.

Flood resilience planning based on structured decision making and the eco-decisional network tool - A Louisiana case study.

Optimal container resource allocation in cloud architecture: A new hybrid model

Multimedia traffic is predicted to account for 82% of the total data traffic by the year 2020. With the increasing popularity of video streaming applications like YouTube, Netflix, Amazon Prime Video, popular video content is often required to be delivered to a large audience simultaneously. Multicast transmission can be used to cater to such applications efficiently. The common content can be transmitted to the users on the same resources resulting in considerable resource conservation. This paper proposes various schemes for efficient grouping and resource allocation for multicast transmission in LTE. The optimal grouping and resource allocation problems are shown to be NP-hard and so, we propose heuristic algorithms for both these problems. We also formulate a Simulated Annealing based algorithm to approximate the optimal resource allocation for our problem. The LP-relaxation based resource allocation proposed by us results in allocations very close to the estimated optimal.

In the contemporary era marked by escalating crises, from natural disaster to cyber-attack, effective crisis management and emergency response have become imperative. This study investigates the profound influence of cutting-edge information technology (IT) on crisis management, accentuating the concept of "Techno-Resilience" — the interplay between advanced IT and the resilience of communities and organizations in navigating, adapting to, and recovering from crises. Emphasizing disaster preparedness and response efficiency, this research explores how IT tools, including Geographic Information Systems (GIS), real-time data analytics, and communication systems, augment situational awareness, optimize resource allocation, and streamline coordination among stakeholders during crises. Employing a mixed-method approach encompassing qualitative interviews, surveys, and statistical analyses, this study meticulously examines the transformative capabilities of Techno-Resilience within crisis management frameworks. Ethical considerations were rigorously observed throughout the data collection and analysis phases. Findings unveil critical areas necessitating improvement in organizational readiness for crisis management, particularly focusing on technology availability, employee training, data security, communication efficacy, and system scalability. Additionally, an assessment of employee readiness illuminates strengths and weaknesses, directing tailored strategies to fortify their preparedness in crisis scenarios. Economic impact analyses underscore the financial advantages of robust crisis management strategies, emphasizing their role in reducing revenue loss, mitigating reputation repair costs, and safeguarding shareholder value. Insights derived from crisis preparedness statistics and crisis management investments pinpoint opportunities for organizations to bolster their crisis management approaches and allocate resources strategically. Projections within the global Crisis Emergency and Incident Management Platforms market underline the escalating significance of these platforms in managing crises on a global scale. This comprehensive study amalgamates multifaceted insights, offering a roadmap for organizations and policymakers to fortify their crisis response frameworks for a more resilient future.