Design of evacuation plans for densely urbanised city centres

The high population density and tightly packed nature of some city centres make emergency planning for these urban spaces especially important, given the potential for human loss in case of disaster. Historic and recent events have made emergency service planners particularly conscious of the need for preparing evacuation plans in advance. This paper discusses a methodological approach for assisting decision-makers in designing urban evacuation plans. The approach aims at quickly and safely moving the population away from the danger zone into shelters. The plans include determining the number and location of rescue facilities, as well as the paths that people should take from their building to their assigned shelter in case of an occurrence requiring evacuation. The approach is thus of the location–allocation–routing type, through the existing streets network, and takes into account the trade-offs among different aspects of evacuation actions that inevitably come up during the planning stage. All the steps of the procedure are discussed and systematised, along with computational and practical implementation issues, in the context of a case study – the design of evacuation plans for the historical centre of an old European city

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

Air Force Institute of Technology Research Report 2019

This Research Report presents the FY19 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Computer simulation of pedestrian dynamics at high densities

The increasing importance and magnitude of large-scale events in our society calls for continuous research in the field of pedestrian dynamics. This dissertation investigates the dynamics of pedestrian motion at high densities using computer simulations of stochastic models. The first part discusses the successful application of the Floor Field Cellular Automaton (FFCA) in an evacuation assistant that performs faster than real-time evacuation simulations of up to $50,000$ persons leaving a multi-purpose arena. A new interpretation of the matrix of preference improves the realism of the FFCA simulation in U-turns, for instance at the entrance to the stands. The focus of the second part is the experimentally observed feature of phase separation in pedestrian dynamics into a slow-moving and a completely jammed phase. This kind of phase separation is fundamentally different to known instances of phase separation in e.g. vehicular traffic. Different approaches to modeling the phase separation are discussed and an investigation of both established and new models of pedestrian dynamics illustrates the difficulties of finding a model able to reproduce the phenomenon. The Stochastic Headway Dependent Velocity Model is introduced and extensively analyzed, simulations of the model evolve into a phase-separated state in accordance with the experimental data. Key components of the model are its slow-to-start rule, minimum velocity, and large interaction range

Validation Risk Across Hierarchical Models

Modeling and simulation are applied in a great many methods across a variety of topics. Model developers and users alike have a professional duty to understand the complexities of the tools and methods they are used. Oftentimes, models that have been independently constructed and executed are used to inform one another for an analytic purpose and the compatibility of the models is not always addressed. In the literature, great attention has been paid to model validation. When using models constructively with one another, analysts must understand the bounds of model validity and ensure that the combination of models does not generate poor information. The literature reveals significant research on model interoperability and model composability. Special analytic cases of composability in multi-resolution modeling have also been examined in the available research. What is not available, however, is the ability to assess models’ ability to inform one another without violating the validation of either model. Therefore, the purpose of this research is to develop a risk of method to model composability. To develop this method, a macroscopic model, simulating large-scale transportation problems will be implemented. An available technique for Model Use Risk Methodology (MURM) will be applied to the macroscopic model to measure its appropriateness for use within its validated space. The model will be decomposed into atomic units of Objects and Processes. Next, a microscopic traffic model will be similarly decomposed into atomic units and be used to inform the macroscopic model. Applying model similarity techniques across the atoms of both models will yield an assessment of their compatibility of one another. The macroscopic model will be reassessed using the MURM. Changes in its risk-of-use score will be compared against the model elements’ similarity to derive a relationship between model similarity and its impact upon model use appropriateness

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Models, Theoretical Properties, and Solution Approaches for Stochastic Programming with Endogenous Uncertainty

In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under endogenous uncertainty and demonstrate their benefits in some application areas. First, we develop a data-driven stochastic program (integrates a supervised machine learning algorithm to estimate probability distributions of uncertain parameters) for a wildfire risk reduction problem, where resource allocation decisions probabilistically affect uncertain human behavior. The nonconvex model is linearized using a reformulation approach. To solve a realistic-sized problem, we introduce a simulation program to efficiently compute the recourse objective value for a large number of scenarios. We present managerial insights derived from the results obtained based on Santa Fe National Forest data. Second, we develop a data-driven stochastic program with both endogenous and exogenous uncertainties with an application to combined infrastructure protection and network design problem. In the proposed model, some first-stage decision variables affect probability distributions, whereas others do not. We propose an exact reformulation for linearizing the nonconvex model and provide a theoretical justification of it. We designed an accelerated L-shaped decomposition algorithm to solve the linearized model. Results obtained using transportation networks created based on the southeastern U.S. provide several key insights for practitioners in using this proposed methodology. Finally, we study submodular optimization under endogenous uncertainty with an application to complex system reliability. Specifically, we prove that our stochastic program\u27s reliability maximization objective function is submodular under some probability distributions commonly used in reliability literature. Utilizing the submodularity, we implement a continuous approximation algorithm capable of solving large-scale problems. We conduct a case study demonstrating the computational efficiency of the algorithm and providing insights