Assessment of crisis readiness to move a patient from the airport with suspected Ebola

The aim of this article is to verify the readiness of patient transport from the airport with symptoms for Ebola disease by the rescue services of the Integrated Rescue System of the Czech Republic. Detection of possible risks and causes of risks during patient transport. In one part of the article, the part is devoted to the current legislation regulating the cooperation of IRS, economic measures for crisis situations, functions of state material reserves management, material security of selected IRS components and the work of BIOHAZARD TEAM. The main part of the article describes the course of the extraordinary event. There is a chapter devoted to the analysis and evaluation of risks during transport. It also deals with the issues of transport, risks and problems that may be encountered by the intervening members of the IRS units. In conclusion, the proposed measures to help minimize risks in the transport of infected patient

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

CASUALTY EVACUATION OPTIMIZATION IN A CONFLICTED ENVIRONMENT

Servicemembers who are injured, particularly in combat, often require rapid evacuation and transport through contested environments. Using unmanned autonomous vehicles (UAV) may help reduce the personnel required to move patients to points of care, thereby reducing the potential for further casualties. However, the UAV and the original patient may still be subject to detection by enemy agents in the area. Safely transporting a casualty in as little time as possible greatly improves survivability. Current treatment of the problem of moving casualties involves manned medical evacuation (MEDEVAC) missions, often with armed escorts. Autonomous evacuation will likely involve simple shortest path solutions to move from one point to another; however, this will not help protect from adversaries. Our model uses network flow optimization to best determine a safe path for autonomous casualty evacuation to follow, while avoiding adversaries and their attacks, and delivering a patient in a timely fashion. This model synchronizes departure and travel times of two echelons of vehicles to effect patient transfer for extraction to definitive care. With two scenarios, our results prove the concept of this model, successfully delivering patients with synchronized efforts, within time limits, and solving the problem in little computational time.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Multi-agent Spatiotemporal Simulation of Autonomous Vehicle Fleet Operation

Autonomous vehicle fleets, consisting of self-driving vehicles, are at the forefront of transportation innovation. The appearance of autonomous vehicles (AVs) provides a new solution for traffic problems and a new market for transportation network companies such as DiDi and Uber. Conducting simulations in the present is indeed crucial to prepare for the eventual operation of autonomous vehicles, as their widespread adoption is expected to occur in the near future. This research adopts an Agent-Based Modelling (ABM) approach to understand and optimize the performance of autonomous vehicle systems. Moreover, Geographic Information System (GIS) technology also plays a crucial role in enhancing the effectiveness and accuracy of the simulation process. GIS enables the representation and manipulation of geospatial data, such as road networks, land-use patterns, and population distribution. The combination of ABM and GIS allows for the incorporation of real-world geographic data, providing a realistic and geographically accurate environment for the agents in the virtual environment. In this thesis, the multi-agent spatiotemporal simulation is conducted by the GAMA platform. The model simulates the behaviour and interactions of individual agents, which are fleet agents and commuters, to observe the emergent behaviour of the entire system. Within the experiment, different scenarios are considered for both people and fleets to explore a range of approaches and strategies. These scenarios aim to evaluate the effectiveness of various approaches in meeting dynamic commute needs and optimizing fleet operations. By simulating these different scenarios and analyzing their outcomes, the study aims to provide insights into the improvement of fleet size and deployment in autonomous vehicle systems. The ultimate goal is to identify effective strategies that lead to optimized fleet size in different scenarios, reduced idling time and emission, improved traffic management, and overall more efficient and sustainable autonomous vehicle systems

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

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

The Autonomous Attack Aviation Problem

An autonomous unmanned combat aerial vehicle (AUCAV) performing an air-to-ground attack mission must make sequential targeting and routing decisions under uncertainty. We formulate a Markov decision process model of this autonomous attack aviation problem (A3P) and solve it using an approximate dynamic programming (ADP) approach. We develop an approximate policy iteration algorithm that implements a least squares temporal difference learning mechanism to solve the A3P. Basis functions are developed and tested for application within the ADP algorithm. The ADP policy is compared to a benchmark policy, the DROP policy, which is determined by repeatedly solving a deterministic orienteering problem as the system evolves. Designed computational experiments of eight problem instances are conducted to compare the two policies with respect to their quality of solution, computational efficiency, and robustness. The ADP policy is superior in 2 of 8 problem instances - those instances with less AUCAV fuel and a low target arrival rate - whereas the DROP policy is superior in 6 of 8 problem instances. The ADP policy outperforms the DROP policy with respect to computational efficiency in all problem instances

Evaluating the effects of road hump on speed and noise level at a university setting

This study is carried out to determine the effectivness of road humps to reduce the traffic speed and traffic noise in institutional area. The difference in hump profiles in terms of height, width and length are the main factors in determing the effectiveness of road humps. The difference in the profiles of the road hump will cause changing driver behaviour of the users especially when approaching the road hump. The road humps with different design profiles are selected to measure the speed and noise level of the vehicles at, before and after each of the selected road humps. Radar speed gun and noise level meters are used to measure speed and noise level of the vehicles at each of designated points along the major circular road in IIUM. The changes in speed and noise level at different selected points at each of the different profiles of the road humps are the expected findings of this study

Evaluating the effects of road hump on the speed of vehicles in an institutional environment

Vehicles travelling at speed above the permissible speed limit have jeopardized the safety of road users. The concern is greater at institutional environment whereby most road users travel by walking. Road hump is considered as an efficient traffic calming measure in reducing the speed of the vehicle. This paper investigates the effects of different road hump dimensions in decreasing the speed of vehicles at the main road of International Islamic University Malaysia. Six (6) road humps with different design profile were selected. The design profile and spot speed of the vehicles at all six (6) road humps were measured. The speed of vehicles at the road hump was analyzed by using descriptive analysis and t-test. The findings of this study suggest that road hump is effective in lowering the speed of vehicles in an institutional environment. The dimensions of road hump, especially height, influence significantly the speed reduction of vehicles