When an emergency occurs, hazard evolution simulators are a very helpful tool for the teams in charge of making decisions. These simulators need certain input data, which defines the characteristics of the environment...
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When an emergency occurs, hazard evolution simulators are a very helpful tool for the teams in charge of making decisions. These simulators need certain input data, which defines the characteristics of the environment where the emergency is taking place. This kind of data usually constitutes a big set of parameters, which have been previously recorded from observations, usually coming from remote sensors, pictures, etc. However, this data is frequently subject to a high degree of uncertainty, as well as the results produced by the corresponding simulators. Hence, it is also necessary to pay attention to the simulations’ quality and reliability. In this work we expose the way we deal with such uncertainty. Our research group has previously developed a two-stage prediction methodology that introduces an adjustment stage in order to deal with the uncertainty on the simulator input parameters. This method significantly improves predictions’ quality, however, in order to be useful, a good characterization of the adjustment techniques has to be carried out so that we are able to choose the best configuration of them, given certain restrictions regarding resources availability and time deadlines. In this work, we focus on forest fires spread prediction as a real study case, for which Genetic Algorithms (GA) have been demonstrated to be a suitable adjustment strategy. We describe the methodology used to characterize the GA and we also validate it when assessing in advance the quality of the fire spread prediction.
This article presents an Agent-Based modeling (ABM) simulation to design a decision support system (DSS) for Healthcare Emergency department (ED). This DSS aims to aid EDs heads in setting up management guidelines to ...
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This article presents an Agent-Based modeling (ABM) simulation to design a decision support system (DSS) for Healthcare Emergency department (ED). This DSS aims to aid EDs heads in setting up management guidelines to improve the operation of EDs. This ongoing research is being performed by the Research Group in Individual Oriented Modeling (IoM) at the University Autonoma of Barcelona (UAB) with close collaboration of Hospital ED Staff Team. The objective of the proposed ABM procedure is to optimize the performance of such complex and dynamic Healthcare EDs, because worldwide most of them are overcrowded, and unable to provide ad hoc care, quality and service. Exhaustive search (ES) optimization is used to find out the optimal ED staff configuration, which includes doctors, triage nurses, and admission personnel, i.e., a multidimensional problem. An index is proposed to minimize patient length of stay in the ED. The results obtained by using an alternative pipeline scheme to ES are promising and a better understanding of the problem is achieved. The impact of the pipeline scheme to reduce the computational cost of exhaustive search is outlined.
This paper proposes a prediction engine designed for non-dedicated clusters, which is able to estimate the turnaround time for parallel applications, even in the presence of serial workload of the workstation owner. T...
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This paper proposes a prediction engine designed for non-dedicated clusters, which is able to estimate the turnaround time for parallel applications, even in the presence of serial workload of the workstation owner. The prediction engine can be configured to work with three different estimation kernels： a Historical kernel, a Simulation kernel based on analytical models and an integration of both, named Hybrid kernel. These estimation proposals were integrated into a scheduling system, named CISNE, which can be executed in an on-line or off-line mode. The accuracy of the proposed estimation methods was evaluated in relation to different job scheduling policies in a real and a simulated cluster environment. In both environments, we observed that the Hybrid system gives the best results because it combines the ability of a simulation engine to capture the dynamism of a non-dedicated environment together with the accuracy of the historical methods to estimate the application runtime considering the state of the resources.
In high performance clusters current parallel application communication needs such as traffic pattern, communication volume, etc., change along time and are difficult to know in advance. Such needs often exceed or do ...
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This paper presents the results of an ongoing project that is being carried out by the Research Group in Individual Oriented Modelling (IoM) of the University Autonoma of Barcelona (UAB) with the participation of Hosp...
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ISBN:
(纸本)9783642198748
This paper presents the results of an ongoing project that is being carried out by the Research Group in Individual Oriented Modelling (IoM) of the University Autonoma of Barcelona (UAB) with the participation of Hospital Emergency department (ED) Staff Teams. Its general objective is creating a simulator that, used as decision support system (DSS), aids the heads of the ED to make the best informed decisions possible. The defined ED model is a pure Agent-Based Model, formed entirely of the rules governing the behaviour of the individual agents which populate the system. The actions of agents and the communication between them are represented using Moore state machines extended to include probabilistic transitions. The model also includes the environment in which agents move and interact. With the aim of verifying the proposed model an initial simulation has been created using NetLogo.
Individual-oriented simulation allows us to represent the global behavior of a system through local interaction in discrete time steps. As we face up close-to-reality models and large-scale workloads, we focus on turn...
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Individual-oriented simulation allows us to represent the global behavior of a system through local interaction in discrete time steps. As we face up close-to-reality models and large-scale workloads, we focus on turning from traditional approaches towards distributed simulation in order to obtain more accurate results in less time. One of the main problems in distributed simulation is how to distribute individuals efficiently through distributed architecture. Individual-oriented systems can be implemented in a distributed fashion by using either a grid-based or cluster-based approach. On one hand, grid-based approaches consist of assigning to each node a simulation space portion, together with the set of individuals currently residing in that area. On the other hand, cluster-based approaches consist of assigning to each node a fixed set of individuals. In this work we present a cluster-based method based on Voronoi diagrams and covering radius criterion in order to avoid unnecessary interaction between individuals. We can show experimentally that our proposal reduces the communication and computing times significantly increasing simulation efficiency.
This paper presents an Agent-Based modeling and simulation to design a decision support system (DSS) for the operation of Healthcare Emergency departments (ED). This DSS aims to aid EDs managers in setting up strategi...
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This paper presents an Agent-Based modeling and simulation to design a decision support system (DSS) for the operation of Healthcare Emergency departments (ED). This DSS aims to aid EDs managers in setting up strategies and management guidelines to optimize the operation of EDs. This ongoing research is being performed by the Research Group on Individual Oriented Modeling (IoM) of CAOS in the University Autonoma of Barcelona (UAB) in close collaboration with Hospital ED Staff. The simulation main objective is to optimize the performance of such complex and dynamic Healthcare ED. Optimization is performed to find the optimal ED staff configuration, which consists of doctors, triage nurses, and admission personnel, i.e. a multidimensional problem. Two different indexes, to minimize patient waiting time, and to maximize patient throughput, were proposed and tested and their results obtained appying an exhaustive search technique, yield promising results and better understanding of the problem.
Modeling and simulation have been shown to be useful tools in many areas of the Healthcare operational management, field in which there is probably no area more dynamic and complex than hospital emergency departments ...
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Modeling and simulation have been shown to be useful tools in many areas of the Healthcare operational management, field in which there is probably no area more dynamic and complex than hospital emergency departments (ED). This paper presents the results of an ongoing project that is being carried out by the Research Group in Individual Oriented Modeling (IoM) of the University Autonoma of Barcelona (UAB) with the participation of Hospital of Sabadell ED Staff Team. Its general objective is creating a simulator that, used as decision support system (DSS), aids the heads of the ED to make the best informed decisions possible. The defined ED model is a pure Agent-Based Model, formed entirely of the rules governing the behavior of the individual agents which populate the system. Two distinct types of agents have been identified, active and passive. Active agents represent human actors, meanwhile passive agents represent services and other reactive systems. The actions of agents and the communication between them will be represented using Moore state machines extended to include probabilistic transitions. The model also includes the environment in which agents move and interact. With the aim of verifying the proposed model an initial simulation has been created using NetLogo, an agent-based simulation environment well suited for modeling complex systems.
The interconnection network communicates and links together the processing units of modern high-performance computing systems. In this context, network faults have an extremely high impact since most routing algorithm...
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Computational simulation has been used as a powerful tool to represent the dynamical behavior of systems based on complex analytic models. These types of models have two main drawbacks: (a) limitations due to the degr...
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Computational simulation has been used as a powerful tool to represent the dynamical behavior of systems based on complex analytic models. These types of models have two main drawbacks: (a) limitations due to the degree of abstraction needed to simulate them, (b) high computing power to simulate a heavily simplified models. The computing power available today can overcome these limitations to perform quicker simulations of complex models that are closer to reality. In this paper, the experiments and performance analysis of a distributed simulation for a complex individual oriented model (fish schools) are presented. The development of the fish school simulator includes the possibility of working with large models that include large numbers of fish (>10 6 of individuals), predators and obstacles in the simulated world.
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