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This volume results from the “Second International Conference on Dynamics of Disasters” held in Kalamata, Greece, June 29-July 2, 2015. The conference covered particular topics involved in natural and man-made disasters such as war, chemical spills, and wildfires. Papers in this volume examine the finer points of disasters through: Critical infrastructure protection Resiliency Humanitarian logistic Relief supply chains Cooperative game theory Dynamical systems Decision making under risk and uncertainty Spread of diseases Contagion Funding for disaster relief Tools for emergency preparedness Response, and risk mitigation Multi-disciplinary theories, tools, techniques and methodologies are linked with disasters from mitigation and preparedness to response and recovery. The interdisciplinary approach to problems in economics, optimization, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.
This book surveys new algorithmic approaches and applications to natural and man-made disasters such as oil spills, hurricanes, earthquakes and wildfires. Based on the “Third International Conference on Dynamics of Disasters” held in Kalamata, Greece, July 2017, this Work includes contributions in evacuation logistics, disaster communications between first responders, disaster relief, and a case study on humanitarian logistics. Multi-disciplinary theories, tools, techniques and methodologies are linked with disasters from mitigation and preparedness to response and recovery. The interdisciplinary approach to problems in economics, optimization, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.
The COVID-19 pandemic has vividly and dramatically demonstrated the importance of supply chains to the functioning of societies and our economies. The discussion in this timely book explores prominent issues concerning supply chain networks and labor. The readership is aimed to include students, researchers, practitioners, and policy-makers, interested in the wide range of topics presented in these pages. Labor has a particular focus as the driver behind supply chains, whether associated with food products, life-saving medicines and supplies, or high tech products that make innovation possible, just to name a few. The impacts of policy interventions, in the form of wage bounds, and their ramifications, in terms of volume of attracted labor, product prices, product volumes, as well as profits, are explored. Profit-maximizing firms are considered (with relevant associated issues such as waste management in the case of the food sector, for example), but also non-profits, as in blood services, as well as humanitarian organizations engaged in disaster relief. The book is filled with many network figures, graphs, and tables with data, both input and output and includes an appendix that provides the foundations of the underlying mathematical methodologies used. The book offers strong evidence for the need to provide a holistic, system-wide perspective for the modeling, analysis, and solution of supply chain problems with the inclusion of the critical labor resources. A formalism using the prism of supply chain networks, which yields a graphic representation of supply chains, consisting of multiple stakeholders, is constructed. Models that capture the behaviors and interactions of single decision-makers as well as multiple decision-makers engaged in supply chain activities of production, transportation, storage, and distribution, are considered. The models capture many realistic constraints faced by firms today, as they seek to produce and deliver products, while dealing with competition, various constraints on labor, a variety of disruptions, labor shortages, challenges associated with proper wage-determination, plus the computation of optimal investments in labor productivity subject to budget constraints. The book provides prescriptive suggestions in terms of how to ameliorate negative impacts of labor disruptions and demonstrate benefits of appropriate wage determination.
This contributed volume discusses aspects of nonlinear analysis in which optimization plays an important role, as well as topics which are applied to the study of optimization problems. Topics include set-valued analysis, mixed concave-convex sub-superlinear Schroedinger equation, Schroedinger equations in nonlinear optics, exponentially convex functions, optimal lot size under the occurrence of imperfect quality items, generalized equilibrium problems, artificial topologies on a relativistic spacetime, equilibrium points in the restricted three-body problem, optimization models for networks of organ transplants, network curvature measures, error analysis through energy minimization and stability problems, Ekeland variational principles in 2-local Branciari metric spaces, frictional dynamic problems, norm estimates for composite operators, operator factorization and solution of second-order nonlinear difference equations, degenerate Kirchhoff-type inclusion problems, and more.
In answer to the unprecedented challenges and threats that face today’s globalized world, the primary goal of this Handbook is to identify the most probable threats that have affected humanity in recent years and our world in years to come. The Handbook comprises mostly expository chapters that discuss tested methods/algorithms, case studies, as well as policy decision-making techniques surrounding threats and unnatural disasters, to evaluate their effects on people and to propose ways to mitigate these effects. In several chapters, new approaches and suggested policies supplement algorithms that are already in practice. The curated content brings together key experts from the academic and policy worlds to formulate a guide of principal techniques employed to gain better control over selected types of threats. This Handbook explores a wide range of technologies and theories and their impact on countering threats. These include artificial intelligence, machine learning, variational inequality theory, game theory, data envelopment analysis, and data-driven risk analysis. These tools play a vital role in decision-making processes and aid in finding optimal solutions. Additionally, a variety of optimization techniques are employed. These include (mixed) integer linear programming models for identifying critical nodes in complex systems, heuristics, approximation algorithms, and bilevel mixed integer programming for determining the most impactful links in dynamic networks. Furthermore, simulation tools are described that enable the quantification of societal resilience. These techniques collectively provide a mathematical framework capable of quantifying fundamental aspects of threats. They equip policymakers with the necessary tools and knowledge to minimize the impact of unnatural threats. The expected readership is wide and includes officials working in technical and policy roles in various ministries such as the Ministry of Defense, Civil Protection, Ministry of Public Order and Citizen Protection, United Nations, European Institutions for Threat Management, NATO, Intelligence Agencies, Centers of Excellence for Countering Threats, Think Tanks, Centers for Policy Studies, Political Leaders, the European Commission, National Institutes, International Organizations, Strategic Consulting Experts, Policymakers, and Foreign Affairs personnel. Some of these national or international organizations employ algorithms to measure resilience and enhance security. Quantification is challenging but crucial in the scenarios discussed in the book. This Handbook will also prove valuable to various universities (non-practitioners), studying systems engineering, leadership, management, strategy, foreign affairs, politics, and related disciplines.
Disaster Relief Aid: Changes and Challenges provides a comprehensive analysis of disaster relief efforts undertaken globally during the last several decades, and examines the changes and challenges that have emerged over time. The book evaluates the current state of disaster relief and discusses how it may be improved. The author examines salient features of disaster relief operations and provides an overview of the development of global humanitarian assistance programs. The book also explores how disaster aid is channelled from non-affected areas to affected areas. Using five major natural and man-made disasters as case studies, the book analyses the nature and extent of emergency relief efforts undertaken for each. The final chapter covers the post-disaster convergence phenomenon; outlines the major challenges of international disaster relief operation and finally, posits recommendations on how to improve future disaster relief efforts. This is an essential interdisciplinary text on disaster response for both undergraduate and graduate students as well as an invaluable resource for disaster researchers, managers, and numerous international and national non-governmental organizations (NGOs) and international agencies.
This book is addressed to researchers in operations research, data science and artificial intelligence. It collects selected contributions from the first hybrid “Optimization and Decision Science - ODS2021” international conference on the theme Optimization and Artificial Intelligence and Data Sciences, which was held in Rome 14-17 September 2021 and organized by AIRO, the Italian Operations Research Society and the Department of Statistical Sciences of Sapienza University of Rome. The book offers new and original contributions on different methodological optimization topics, from Support Vector Machines to Game Theory Network Models, from Mathematical Programming to Heuristic Algorithms, and Optimization Methods for a number of emerging problems from Truck and Drone delivery to Risk Assessment, from Power Networks Design to Portfolio Optimization. The articles in the book can give a significant edge to the general themes of sustainability and pollution reduction, distributive logistics, healthcare management in pandemic scenarios and clinical trials, distributed computing, scheduling, and many others. For these reasons, the book is aimed not only at researchers in the Operations Research community but also for practitioners facing decision-making problems in these areas and to students and researchers from other disciplines, including Artificial Intelligence, Computer Sciences, Finance, Mathematics, and Engineering.
Acute events of natural origin, spanning atmospheric, biological, geophysical, hydrologic, and oceanographic realms, persistently menace societies globally. Approximately 160 million people annually bear the brunt of these disasters, with certain regions facing disproportionate impacts. The lack of predictability intensifies the challenge, creating intercommunal capacity gaps and amplifying the dire consequences. Utilizing AI and Machine Learning for Natural Disaster Management provides instances of ML in predicting earthquakes. By leveraging seismic data, AI systems can analyze magnitude and patterns, providing invaluable insights to forecast earthquake occurrences and aftershocks. Similarly, the book unveils the potential of ML in simulating floods by recording and analyzing rainfall patterns from previous years. The predictive power extends to hurricanes, where data on wind speed, rainfall, temperature, and moisture converge to anticipate future occurrences, potentially saving millions in property damage.
In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction. By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.