PERMUTATIONAL METHODS FOR PERFORMANCE ANALYSIS OF STOCHASTIC FLOW NETWORKS

2013 ◽  
Vol 28 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Ilya Gertsbakh ◽  
Reuven Rubinstein ◽  
Yoseph Shpungin ◽  
Radislav Vaisman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Analysis ◽  
Failure Probability ◽  
Stochastic Flow ◽  
Maximal Flow ◽  
Convergence Properties ◽  
Flow Networks ◽  
Flow Network ◽  
Random Failures

In this paper we show how the permutation Monte Carlo method, originally developed for reliability networks, can be successfully adapted for stochastic flow networks, and in particular for estimation of the probability that the maximal flow in such a network is above some fixed level, called the threshold. A stochastic flow network is defined as one, where the edges are subject to random failures. A failed edge is assumed to be erased (broken) and, thus, not able to deliver any flow. We consider two models; one where the edges fail with the same failure probability and another where they fail with different failure probabilities. For each model we construct a different algorithm for estimation of the desired probability; in the former case it is based on the well known notion of the D-spectrum and in the later one—on the permutational Monte Carlo. We discuss the convergence properties of our estimators and present supportive numerical results.

Characterizing stochastic flow networks using the monte carlo method

Networks ◽  
1991 ◽  
Vol 21 (7) ◽  
pp. 775-798 ◽  
Author(s):  
Christos Alexopoulos ◽  
George S. Fishman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Stochastic Flow ◽  
Flow Networks ◽  
The Monte Carlo Method

Reliability Analysis of Embankment Slope in Yaan-Luku Expressway

2011 ◽  
Vol 71-78 ◽  
pp. 1360-1365
Author(s):  
Jian Quan Ma ◽  
Guang Jie Li ◽  
Shi Bo Li ◽  
Pei Hua Xu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Latin Hypercube Sampling ◽  
Natural State ◽  
State Function ◽  
Embankment Slope ◽  
The Times ◽  
Distribution Type

Take a typical cross-section of rockfill embankment slope in Yaan-Luku highway as the research object, reliability analysis is studied under the condition of water table of 840.85m, 851.50m, and loading condition of natural state and horizontal seismic acceleration of 0.2g, respectively. Raw data use Kolmogorov-Smirnov test (K-S test) to determine the distribution type of parametric variation. And the parameters were sampling with Latin hypercube sampling (LHS) method and Monte Carlo (MC) method, respectively, to obtain state function and determine safety factors and reliability indexes. A conclusion is drawn that the times of simulation based on LHS method were less than Monte Carlo method. Also the convergence of failure probability is better than the Monte Carlo method. The safety factor is greater than one and the failure probability has reached to 35.45% in condition of earthquake, which indicating that the instability of rockfill embankment slope is still possible.

An Improved FOSM Method for Calculating the Failure Probability of Welded Pipes with Cracks

2004 ◽  
Vol 261-263 ◽  
pp. 561-566
Author(s):  
Li Xing Huo ◽  
Min Liu ◽  
You Feng Zhang ◽  
Fang Juan Qi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Failure Probability ◽  
Safety Assessment ◽  
Linear Expansion ◽  
Failure Function ◽  
The Monte Carlo Method

To increase the accuracy of R-F method, it is necessary to solve the problems of the linear expansion of failure function and non-normal variables. In this paper, the improved FOSM method was applied to calculate the failure probability of welded pipes with cracks. The examples show that this method is simple, efficient and accurate for reliability safety assessment of welded pipes with cracks. It can save more time than the Monte Carlo method does, so that the improved FOSM method is recommended for general engineering reliability safety assessment of welded pipes with cracks.

A Comparative Study of Different Approaches for Finding the Upper Boundary Points in Stochastic-Flow Networks

2014 ◽  
Vol 10 (3) ◽  
pp. 13-23 ◽  
Author(s):  
Seyed Mehdi Mansourzadeh ◽  
Seyed Hadi Nasseri ◽  
Majid Forghani-elahabad ◽  
Ali Ebrahimnejad
Keyword(s):  
Information System ◽  
Comparative Study ◽  
Test Problems ◽  
Stochastic Flow ◽  
Flow Networks ◽  
Flow Network ◽  
Random Test ◽  
Boundary Points ◽  
Exclusion Method ◽  
Upper Boundary

An information system network (ISN) can be modeled as a stochastic-flow network (SFN). There are several algorithms to evaluate reliability of an SFN in terms of Minimal Cuts (MCs). The existing algorithms commonly first find all the upper boundary points (called d-MCs) in an SFN, and then determine the reliability of the network using some approaches such as inclusion-exclusion method, sum of disjoint products, etc. However, most of the algorithms have been compared via complexity results or through one or two benchmark networks. Thus, comparing those algorithms through random test problems can be desired. Here, the authors first state a simple improved algorithm. Then, by generating a number of random test problems and implementing the algorithms in MATLAB, the proposed algorithm is demonstrated to be more efficient than some existing ones in medium-sized networks. The performance profile introduced by Dolan and More is used for analyzing the output of programs.

Failure probability estimation of steam turbine blades by enhanced Monte Carlo Method

2015 ◽  
Vol 56 ◽  
pp. 80-88 ◽  
Author(s):  
J.A. Rodríguez ◽  
J.C. Garcia ◽  
E. Alonso ◽  
Y. El Hamzaoui ◽  
J.M. Rodríguez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Steam Turbine ◽  
Failure Probability ◽  
Turbine Blades ◽  
Probability Estimation ◽  
Steam Turbine Blades

Performance Analysis of Dual Vt Domino Circuits with P-V-T Variations

2011 ◽  
Vol 88-89 ◽  
pp. 326-330 ◽  
Author(s):  
Jin Hui Wang ◽  
Na Gong ◽  
Gang Liu ◽  
Shu Qin Geng ◽  
Wu Chen Wu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Analysis ◽  
Leakage Current ◽  
Active Power ◽  
Performance Estimation ◽  
Temperature Variations ◽  
Multiple Parameter ◽  
Domino Circuits ◽  
Level Performance

The leakage current, active power and delay characterizations of the domino circuits in the presence of P-V-T (Process, Voltage, and Temperature) variations are analyzed based on multiple-parameter Monte Carlo method. It is demonstrated that failing to account for P-V-T variations and process-electro-thermal couplings can result in significant inaccuracy in transistor-level performance estimation. It also indicates that under significant P-V-T variations, DTV (Dual VtTechnology) is still highly effective to reduce the leakage current and active power for domino circuits, but induces speed penalty. At last, the robustness of different domino circuits with DTV against the P-V-T variations is discussed.

Modeling and Applications on Timed Stochastic-Flow Networks

2015 ◽  
Vol 22 (05) ◽  
pp. 1550023
Author(s):  
Shin-Guang Chen
Keyword(s):  
Network Reliability ◽  
Daily Life ◽  
Transportation Network ◽  
Stochastic Flow ◽  
Production Network ◽  
Flow Networks ◽  
Stochastic Behavior ◽  
Flow Network ◽  
Numerical Examples ◽  
Time Consumption

A stochastic-flow network (SFN) is a network whose flow has stochastic behavior or probabilistic multi-states. A timed stochastic-flow network (TSFN) is a SFN whose flow spends time to go through the network. Traditionally, the evaluation of network reliability does not consider time consumption for the flow to get through the network. However, there are lots of daily-life networks which can be regarded as TSFNs, such as the transportation network, the production network, etc. Their flow spends time to get through the network, and they are not yet explored in the literature. This paper proposes approaches to evaluate the reliability of such networks. Some numerical examples are discussed to illustrate the proposed method.

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