scholarly journals An Investigation of Sequential Sampling Method for Crossdocking Simulation Output Variance Reduction

2008 ◽  
Author(s):  
Adrian Adewunmi ◽  
Uwe Aickelin ◽  
Mike Byrne
Keyword(s):  
Variance Reduction ◽  
Sampling Method ◽  
Sequential Sampling ◽  
Simulation Output ◽  
Output Variance

PEST MANAGEMENT OF DOUGLAS-FIR TUSSOCK MOTH (LEPIDOPTERA: LYMANTRIIDAE): A SEQUENTIAL SAMPLING METHOD TO DETERMINE EGG MASS DENSITY

1984 ◽  
Vol 116 (7) ◽  
pp. 1041-1049 ◽  
Author(s):  
R. F. Shepherd ◽  
I. S. Otvos ◽  
R. J. Chorney
Keyword(s):  
Sampling Method ◽  
Mass Density ◽  
Sequential Sampling ◽  
Douglas Fir ◽  
Egg Mass ◽  
Tussock Moth ◽  
Incipient Stage ◽  
Orgyia Pseudotsugata ◽  
Detection Tool ◽  
Variability Data

AbstractA sequential egg-mass sample system for Douglas-fir tussock moth, Orgyia pseudotsugata McDunnough (Lepidoptera: Lymantriidae), was designed, based on visual scanning of the lower branches of Douglas-fir trees, Pseudotsuga menziesii (Mirb.) Franco. A branch was removed from each quadrant from the upper, middle and lower crown level, and from the lowest whorl of a total of 59 non-defoliated trees in 10 areas. No consistent trend in egg-mass density per branch could be found between crown levels and no level proved superior as a representative of the tree. Therefore, the lower whorl of branches was selected for survey purposes because of sampling efficiency. Sample stop lines were determined from egg-mass density and variability data collected on 55 sites and subsequent defoliation estimates were related to these densities. The system is designed as an early detection tool to be used only in non-defoliated stands at the incipient stage of an impending outbreak.

scholarly journals On the optimal importance process for piecewise deterministic Markov process

2019 ◽  
Vol 23 ◽  
pp. 893-921
Author(s):  
H. Chraibi ◽  
A. Dutfoy ◽  
T. Galtier ◽  
J. Garnier
Keyword(s):  
Importance Sampling ◽  
Simulation Study ◽  
Variance Reduction ◽  
Sampling Method ◽  
Piecewise Deterministic Markov Process ◽  
Importance Sampling Method ◽  
Markovian Processes ◽  
Component Systems ◽  
Reference Measure ◽  
Reduction Methods

In order to assess the reliability of a complex industrial system by simulation, and in reasonable time, variance reduction methods such as importance sampling can be used. We propose an adaptation of this method for a class of multi-component dynamical systems which are modeled by piecewise deterministic Markovian processes (PDMP). We show how to adapt the importance sampling method to PDMP, by introducing a reference measure on the trajectory space. This reference measure makes it possible to identify the admissible importance processes. Then we derive the characteristics of an optimal importance process, and present a convenient and explicit way to build an importance process based on theses characteristics. A simulation study compares our importance sampling method to the crude Monte-Carlo method on a three-component systems. The variance reduction obtained in the simulation study is quite spectacular.

Capacity Expansion Problem by Monte Carlo Sampling Method

2009 ◽  
Vol 13 (6) ◽  
pp. 697-703
Author(s):  
Takayuki Shiina ◽  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Sampling Method ◽  
Sampling Technique ◽  
Capacity Expansion ◽  
Monte Carlo Sampling ◽  
Previous Approach ◽  
Importance Sampling Technique ◽  
Stochastic Vector ◽  
Additive Form

We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.

Study of Sequential Sampling Method for Weibull Distribution

2011 ◽  
Vol 291-294 ◽  
pp. 2195-2198 ◽  
Author(s):  
Xian Zhao Xu ◽  
Hong Liang Lou ◽  
Xing Lin Li
Keyword(s):  
Weibull Distribution ◽  
Sampling Method ◽  
Shape Parameter ◽  
Sequential Sampling ◽  
Acceptance Probability ◽  
Distribution Shape ◽  
Rejection Probability

When sequential sampling method based on MTTF is applied to weibull distribution, shape parameter is considered to be fixed. But, shape parameter in criterions or references is different from that in practice. According to sequential sampling method, with the shape parameter changing, changes of acceptance probability and rejection probability are studied. Finally, the result of simulation evaluating shows that the method of sequential sampling method is feasible and reasonable.

Mode-Pursuing Sampling Method Using Discriminative Coordinate Perturbation for High-Dimensional Expensive Black-Box Optimization

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Yufei Wu ◽  
Teng Long ◽  
Renhe Shi ◽  
G. Gary Wang
Keyword(s):  
Design Optimization ◽  
Sampling Method ◽  
Sequential Sampling ◽  
Computational Cost ◽  
Optimization Methods ◽  
Black Box ◽  
High Dimensional ◽  
Beam Design ◽  
Convergence Performance ◽  
Coordinate Perturbation

Abstract This article presents a novel mode-pursuing sampling method using discriminative coordinate perturbation (MPS-DCP) to further improve the convergence performance of solving high-dimensional, expensive, and black-box (HEB) problems. In MPS-DCP, a discriminative coordinate perturbation strategy is integrated into the original mode-pursuing sampling (MPS) framework for sequential sampling. During optimization, the importance of variables is defined by approximated global sensitivities, while the perturbation probabilities of variables are dynamically adjusted according to the number of optimization stalling iterations. Expensive points considering both optimality and space-filling property are selected from cheap points generated by perturbing the current best point, which balances between global exploration and local exploitation. The convergence property of MPS-DCP is theoretically analyzed. The performance of MPS-DCP is tested on several numerical benchmarks and compared with state-of-the-art metamodel-based design optimization methods for HEB problems. The results indicate that MPS-DCP generally outperforms the competitive methods regarding convergence and robustness performances. Finally, the proposed MPS-DCP is applied to a stepped cantilever beam design optimization problem and an all-electric satellite multidisciplinary design optimization (MDO) problem. The results demonstrate that MPS-DCP can find better feasible optima with the same or less computational cost than the competitive methods, which demonstrates its effectiveness and practicality in solving real-world engineering problems.

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