The Experts below are selected from a list of 9507 Experts worldwide ranked by ideXlab platform
Barry L Nelson - One of the best experts on this subject based on the ideXlab platform.
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input uncertainty quantification for simulation models with piecewise constant non stationary Poisson Arrival processes
Winter Simulation Conference, 2016Co-Authors: Lucy E Morgan, Andrew C Titman, D J Worthington, Barry L NelsonAbstract:Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson Arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.
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Winter Simulation Conference - Input uncertainty quantification for simulation models with piecewise-constant non-stationary Poisson Arrival processes
2016 Winter Simulation Conference (WSC), 2016Co-Authors: Lucy E Morgan, Andrew C Titman, David Worthington, Barry L NelsonAbstract:Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson Arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.
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Transforming Renewal Processes for Simulation of Nonstationary Arrival Processes
INFORMS Journal on Computing, 2009Co-Authors: Ira Gerhardt, Barry L NelsonAbstract:Simulation models of real-life systems often assume stationary (homogeneous) Poisson Arrivals. Therefore, when nonstationary Arrival processes are required, it is natural to assume Poisson Arrivals with a time-varying Arrival rate. For many systems, however, this provides an inaccurate representation of the Arrival process that is either more or less variable than Poisson. In this paper we extend techniques that transform a stationary Poisson Arrival process into a nonstationary Poisson Arrival process (NSPP) by transforming a stationary renewal process into a nonstationary, non-Poisson (NSNP) Arrival process. We show that the desired Arrival rate is achieved and that when the renewal base process is either more or less variable than Poisson, then the NSNP process is also more or less variable, respectively, than an NSPP. We also propose techniques for specifying the renewal base process when presented properties of, or data from, an Arrival process and illustrate them by modeling real Arrival data.
Lucy E Morgan - One of the best experts on this subject based on the ideXlab platform.
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input uncertainty quantification for simulation models with piecewise constant non stationary Poisson Arrival processes
Winter Simulation Conference, 2016Co-Authors: Lucy E Morgan, Andrew C Titman, D J Worthington, Barry L NelsonAbstract:Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson Arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.
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Winter Simulation Conference - Input uncertainty quantification for simulation models with piecewise-constant non-stationary Poisson Arrival processes
2016 Winter Simulation Conference (WSC), 2016Co-Authors: Lucy E Morgan, Andrew C Titman, David Worthington, Barry L NelsonAbstract:Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson Arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.
Denis Pankratov - One of the best experts on this subject based on the ideXlab platform.
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Greedy Bipartite Matching in Random Type Poisson Arrival Model
arXiv: Data Structures and Algorithms, 2018Co-Authors: Allan Borodin, Christodoulos Karavasilis, Denis PankratovAbstract:We introduce a new random input model for bipartite matching which we call the Random Type Poisson Arrival Model. Just like in the known i.i.d. model (introduced by Feldman et al. 2009), online nodes have types in our model. In contrast to the adversarial types studied in the known i.i.d. model, following the random graphs studied in Mastin and Jaillet 2016, in our model each type graph is generated randomly by including each offline node in the neighborhood of an online node with probability $c/n$ independently. In our model, nodes of the same type appear consecutively in the input and the number of times each type node appears is distributed according to the Poisson distribution with parameter 1. We analyze the performance of the simple greedy algorithm under this input model. The performance is controlled by the parameter $c$ and we are able to exactly characterize the competitive ratio for the regimes $c = o(1)$ and $c = \omega(1)$. We also provide a precise bound on the expected size of the matching in the remaining regime of constant $c$. We compare our results to the previous work of Mastin and Jaillet who analyzed the simple greedy algorithm in the $G_{n,n,p}$ model where each online node type occurs exactly once. We essentially show that the approach of Mastin and Jaillet can be extended to work for the Random Type Poisson Arrival Model, although several nontrivial technical challenges need to be overcome. Intuitively, one can view the Random Type Poisson Arrival Model as the $G_{n,n,p}$ model with less randomness; that is, instead of each online node having a new type, each online node has a chance of repeating the previous type.
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greedy bipartite matching in random type Poisson Arrival model
International Workshop and International Workshop on Approximation Randomization and Combinatorial Optimization. Algorithms and Techniques, 2018Co-Authors: Allan Borodin, Christodoulos Karavasilis, Denis PankratovAbstract:We introduce a new random input model for bipartite matching which we call the Random Type Poisson Arrival Model. Just like in the known i.i.d. model (introduced by Feldman et al. [Feldman et al., 2009]), online nodes have types in our model. In contrast to the adversarial types studied in the known i.i.d. model, following the random graphs studied in Mastin and Jaillet [A. Mastin, 2013], in our model each type graph is generated randomly by including each offline node in the neighborhood of an online node with probability c/n independently. In our model, nodes of the same type appear consecutively in the input and the number of times each type node appears is distributed according to the Poisson distribution with parameter 1. We analyze the performance of the simple greedy algorithm under this input model. The performance is controlled by the parameter c and we are able to exactly characterize the competitive ratio for the regimes c = o(1) and c = omega(1). We also provide a precise bound on the expected size of the matching in the remaining regime of constant c. We compare our results to the previous work of Mastin and Jaillet who analyzed the simple greedy algorithm in the G_{n,n,p} model where each online node type occurs exactly once. We essentially show that the approach of Mastin and Jaillet can be extended to work for the Random Type Poisson Arrival Model, although several nontrivial technical challenges need to be overcome. Intuitively, one can view the Random Type Poisson Arrival Model as the G_{n,n,p} model with less randomness; that is, instead of each online node having a new type, each online node has a chance of repeating the previous type.
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APPROX-RANDOM - Greedy Bipartite Matching in Random Type Poisson Arrival Model
2018Co-Authors: Allan Borodin, Christodoulos Karavasilis, Denis PankratovAbstract:We introduce a new random input model for bipartite matching which we call the Random Type Poisson Arrival Model. Just like in the known i.i.d. model (introduced by Feldman et al. [Feldman et al., 2009]), online nodes have types in our model. In contrast to the adversarial types studied in the known i.i.d. model, following the random graphs studied in Mastin and Jaillet [A. Mastin, 2013], in our model each type graph is generated randomly by including each offline node in the neighborhood of an online node with probability c/n independently. In our model, nodes of the same type appear consecutively in the input and the number of times each type node appears is distributed according to the Poisson distribution with parameter 1. We analyze the performance of the simple greedy algorithm under this input model. The performance is controlled by the parameter c and we are able to exactly characterize the competitive ratio for the regimes c = o(1) and c = omega(1). We also provide a precise bound on the expected size of the matching in the remaining regime of constant c. We compare our results to the previous work of Mastin and Jaillet who analyzed the simple greedy algorithm in the G_{n,n,p} model where each online node type occurs exactly once. We essentially show that the approach of Mastin and Jaillet can be extended to work for the Random Type Poisson Arrival Model, although several nontrivial technical challenges need to be overcome. Intuitively, one can view the Random Type Poisson Arrival Model as the G_{n,n,p} model with less randomness; that is, instead of each online node having a new type, each online node has a chance of repeating the previous type.
Andrew C Titman - One of the best experts on this subject based on the ideXlab platform.
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input uncertainty quantification for simulation models with piecewise constant non stationary Poisson Arrival processes
Winter Simulation Conference, 2016Co-Authors: Lucy E Morgan, Andrew C Titman, D J Worthington, Barry L NelsonAbstract:Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson Arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.
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Winter Simulation Conference - Input uncertainty quantification for simulation models with piecewise-constant non-stationary Poisson Arrival processes
2016 Winter Simulation Conference (WSC), 2016Co-Authors: Lucy E Morgan, Andrew C Titman, David Worthington, Barry L NelsonAbstract:Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson Arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.
Kouji Yano - One of the best experts on this subject based on the ideXlab platform.
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On optimal periodic dividend and capital injection strategies for spectrally negative Lévy models
Journal of Applied Probability, 2018Co-Authors: Kei Noba, José-luis Pérez, Kazutoshi Yamazaki, Kouji YanoAbstract:De Finetti’s optimal dividend problem has recently been extended to the case when dividend payments can be made only at Poisson Arrival times. In this paper we consider the version with bail-outs where the surplus must be nonnegative uniformly in time. For a general spectrally negative Levy model, we show the optimality of a Parisian-classical reflection strategy that pays the excess above a given barrier at each Poisson Arrival time and also reflects from below at 0 in the classical sense.
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On optimal periodic dividend and capital injection strategies for spectrally negative L\'evy models
arXiv: Probability, 2017Co-Authors: Kei Noba, José-luis Pérez, Kazutoshi Yamazaki, Kouji YanoAbstract:De Finetti's optimal dividend problem has recently been extended to the case dividend payments can only be made at Poisson Arrival times. This paper considers the version with bail-outs where the surplus must be nonnegative uniformly in time. For a general spectrally negative L\'evy model, we show the optimality of a Parisian-classical reflection strategy that pays the excess above a given barrier at each Poisson Arrival times and also reflects from below at zero in the classical sense.
