Evacuation simulation with limited capacity sinks

We heuristically solve an evacuation problem with limited capacity shelters. An evolutionary learning algorithm is developed for the combined route- and shelter-assignment problem. It is complemented with a heuristic method for the fair minimization of shelter capacities. Different behavioral assumptions fair vs. globally optimal) are investigated. The proposed approaches are discussed in the context of a real-world tsunami evacuation problem

Route Guidance Map for Emergency Evacuation

An efficient process of emergency evacuation must be guided. In the event of an evacuation instruction, a significant amount of time is spent by evacuees looking for a place of relative safety or an exit. Due to the ensuing stress and confusion evacuees try to follow others, consequently, all the exits are not used effectively. Therefore, it is important to develop a route guidance map for the emergency. The focus of the map is to help both, the evacuees and the authorities to perform evacuation efficiently. This paper presents a route guidance map for pedestrians that aims an efficient evacuation in case of an emergency. An agent-based simulation framework is used for the simulation of various scenarios to prepare the guiding map. A real world case study of Sarojini Nagar, Delhi is presented to test the presented methodology. Eventually, several strategic recommendations are provided for improving safety of existing infrastructure

Fairness-Based Transportation Resilience for Communities under Tsunami Hazard

Abstract: Natural disasters such as tsunamis have catastrophic impacts on the functionality and resilience of transportation networks in impacted areas, and they can damage coastal regions hundreds of kilometers away from the earthquake that caused them, resulting in a significant number of casualties. As a result, the ultimate goal of this study was to develop a fair-based evacuation model under tsunami hazards. The proposed fairness-based evacuation model used in this study aimed to give evacuees equal access to emergency facility centers and assembly areas, reducing the number of casualties and assessing the capability of providing the evacuees' needs

Optimization Approaches for Improving Mitigation and Response Operations in Disaster Management

Disasters are calamitous events that severely affect the life conditions of an entire community, being the disasters either nature-based (e.g., earthquake) or man-made (e.g., terroristic attack). Disaster-related issues are usually dealt with according to the Disaster Operations Management (DOM) framework, which is composed of four phases: mitigation and preparedness, which address pre-disaster issues, and response and recovery, which tackle problems arising after the occurrence of a disaster. The ultimate scope of this dissertation is to present novel optimization models and algorithms aimed at improving operations belonging to the mitigation and response phases of the DOM. On the mitigation side, this thesis focuses on the protection of Critical Information Infrastructures (CII), which are commonly deemed to include communication and information networks. The majority of all the other Critical Infrastructures (CI), such as electricity, fuel and water supply as well as transportation systems, are crucially dependent on CII. Therefore, problems associated with CII that disrupt the services they are able to provide (whether to a single end-user or to another CI) are of increasing interest. This dissertation reviews several issues emerging in the Critical Information Infrastructures Protection (CIIP), field such as: how to identify the most critical components of a communication network whose disruption would affect the overall system functioning; how to mitigate the consequences of such calamitous events through protection strategies; and how to design a system which is intrinsically able to hedge against disruptions. To this end, this thesis provides a description of the seminal optimization models that have been developed to address the aforementioned issues in the general field of Critical Infrastructures Protection (CIP). Models are grouped in three categories which address the aforementioned issues: survivability-oriented interdiction, resource allocation strategy, and survivable design models; existing models are reviewed and possible extensions are proposed. In fact, some models have already been developed for CII (i.e., survivability-interdiction and design models), while others have been adapted from the literature on other CI (i.e., resource allocation strategy models). The main gap emerging in the CII field is that CII protection has been quite overlooked which has led to review optimization models that have been developed for the protection of other CI. Hence, this dissertation contributes to the literature in the field by also providing a survey of the multi-level programs that have been developed for protecting supply chains, transportation systems (e.g., railway infrastructures), and utility networks (e.g., power and water supply systems), in order to adapt them for CII protection. Based on the review outcomes, this thesis proposes a novel linear bi-level program for CIIP to mitigate worst-case disruptions through protection investments entailing network design operations, namely the Critical Node Detection Problem with Fortification (CNDPF), which integrates network survivability assessment, resource allocation strategies and design operations. To the best of my knowledge, this is the first bi-level program developed for CIIP. The model is solved through a Super Valid Inequalities (SVI) decomposition approach and a Greedy Constructive and Local Search (GCLS) heuristic. Computational results are reported for real communication networks and for different levels of both disaster magnitude and protection resources. On the response side, this thesis identifies the current challenges in devising realistic and applicable optimization models in the shelter location and evacuation routing context and outlines a roadmap for future research in this topical area. A shelter is a facility where people belonging to a community hit by a disaster are provided with different kinds of services (e.g., medical assistance, food). The role of a shelter is fundamental for two categories of people: those who are unable to make arrangements to other safe places (e.g., family or friends are too far), and those who belong to special-needs populations (e.g., disabled, elderly). People move towards shelter sites, or alternative safe destinations, when they either face or are going to face perilous circumstances. The process of leaving their own houses to seek refuge in safe zones goes under the name of evacuation. Two main types of evacuation can be identified: self-evacuation (or car-based evacuation) where individuals move towards safe sites autonomously, without receiving any kind of assistance from the responder community, and supported evacuation where special-needs populations (e.g., disabled, elderly) require support from emergency services and public authorities to reach some shelter facilities. This dissertation aims at identifying the central issues that should be addressed in a comprehensive shelter location/evacuation routing model. This is achieved by a novel meta-analysis that entail: (1) analysing existing disaster management surveys, (2) reviewing optimization models tackling shelter location and evacuation routing operations, either separately or in an integrated manner, (3) performing a critical analysis of existing papers combining shelter location and evacuation routing, concurrently with the responses of their authors, and (4) comparing the findings of the analysis of the papers with the findings of the existing disaster management surveys. The thesis also provides a discussion on the emergent challenges of shelter location and evacuation routing in optimization such as the need for future optimization models to involve stakeholders, include evacuee as well as system behaviour, be application-oriented rather than theoretical or model-driven, and interdisciplinary and, eventually, outlines a roadmap for future research. Based on the identified challenges, this thesis presents a novel scenario-based mixed-integer program which integrates shelter location, self-evacuation and supported-evacuation decisions, namely the Scenario-Indexed Shelter Location and Evacuation Routing (SISLER) problem. To the best of my knowledges, this is the second model including shelter location, self-evacuation and supported-evacuation however, SISLER deals with them based on the provided meta-analysis. The model is solved through a Branch-and-Cut algorithm of an off-the-shelf software, enriched with valid inequalities adapted from the literature. Computational results are reported for both testbed instances and a realistic case study

Optimizing and Simulating Evacuation in Urban Areas

Diese Doktorarbeit beschäftigt sich mit der Evakuierungsplanung in städtischen Gebieten. Nach einem Literaturüberblick zu Beginn der Arbeit wird das Cell-Transmission model verwendet, um ein grundlegendes mathematisches Optimierungsmodell zur Evakuierungsplanung mit Fahrzeugen zu formulieren. Anschließend werden eine erweiterte Version dieses Optimierungsmodells sowie mehrere heuristische Lösungsverfahren zur Lösung der Optimierungsmodelle vorgestellt. Darüber hinaus werden Erweiterungen der Optimierungsmodelle zur Berücksichtigung von Rettungskräften und Fußgängern in der Evakuierungsplanung betrachtet.This doctoral thesis deals with evacuation planning in urban areas. After a literature review at the beginning of the thesis, the Cell-Transmission model is utilized to formulate a basic mathematical optimization problem for evacuation planning with vehicles. Afterwards, an extended version of this basic optimization problem as well as several heuristic solution procedures to solve the optimization models are presented. Moreover, enhancements of the optimization models in terms of integrating rescue teams as well as pedestrians in evacuation planning are considered

A system dynamics & emergency logistics model for post-disaster relief operations

Emergency teams’ efficiency in responding to disasters is critical in saving lives, reducing suffering, and for damage control. Quality standards for emergency response systems are based on government policies, resources, training, and team readiness and flexibility. This research investigates these matters in regards to Saudi emergency responses to floods in Jeddah in 2009 and again in 2011. The study is relevant to countries who are building emergency response capacity for their populations: analysing the effects of the disaster, communications and data flows for stakeholders, achieving and securing access, finding and rescuing victims, setting up field triage sites, evacuation, and refuges. The research problem in this case was to develop a dynamic systems model capable of managing real time data to allow a team or a decision-maker to optimise their particular response within a rapidly changing situation. The Emergency Logistics Centre capability model responds to this problem by providing a set of nodes relevant to each responsibility centre (Civil Defence, regional/local authority including rescue teams, police and clean-up teams, Red Crescent). These nodes facilitate information on resource use and replenishment, and barriers such as access and weather can be controlled for in the model. The dynamic systems approach builds model capacity and transparency, allowing emergency response decision-makers access to updated instructions and decisions that may affect their capacities. After the event, coordinators and researchers can review data and actions for policy change, resource control, training and communications. In this way, knowledge from the experiences of members of the network is not lost for future position occupants in the emergency response network. The conclusion for this research is that the Saudi emergency response framework is now sufficiently robust to respond to a large scale crisis, such as may occur during the hajj with its three million pilgrims. Researchers are recommended to test their emergency response systems using the Emergency Logistics Centre model, if only to encourage rethinking and flexibility of perhaps stale or formulaic responses from staff. This may lead to benefits in identification of policy change, training, or more appropriate pathways for response teams

The Multi-Agent Transport Simulation MATSim

"The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Incorporating declared capacity uncertainty in optimizing airport slot allocation

Slot allocation is the mechanism used to allocate capacity at congested airports. A number of models have been introduced in the literature aiming to produce airport schedules that optimize the allocation of slot requests to the available airport capacity. A critical parameter affecting the outcome of the slot allocation process is the airport’s declared capacity. Existing airport slot allocation models treat declared capacity as an exogenously defined deterministic parameter. In this presentation we propose a new robust optimization formulation based on the concept of stability radius. The proposed formulation considers endogenously the airport’s declared capacity and expresses it as a function of its throughput. We present results from the application of the proposed approach to a congested airport and we discuss the trade-off between the declared capacity of the airport and the efficiency of the slot allocation process