An efficient algorithm for simultaneously deducing minimal paths as well as cuts of a communication network

Samad, Md. Abdus

doi:10.1016/0026-2714(87)90458-6

Cited by 56 publications

(15 citation statements)

References 9 publications

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“…Monte Carlo Simulation RSM Response Surface Methodology BBD Box-Behnken designed MC/MP minimal pathset/cutset Reliability optimisation has been a popular area of research and has received a significant amount of attention during the past four decades [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] due to reliability Int…”

Section: Mcsmentioning

confidence: 99%

“…In recent years, network reliability theory has been applied extensively to many real-world systems such as oil/ gas production systems [1], computer and communication systems [2,3], power transmission and distribution systems [4], transportation systems [5], etc. System reliability plays an important role in our modern society.…”

Section: Introductionmentioning

confidence: 99%

“…Several authors have presented some approaches to evaluate network reliability. Most of these papers assumed that there is no cost limitation [1,2,3,4,5,6,7,8,9,10,11,12,13,14] or consider the cost limitation only as a constraint [14,15,16,17]; furthermore, none of these papers can be used to provide a good estimate of the reliability function in a complex network. However, it is highly practical and desirable to evaluate the network reliability function and minimise the total reliability cost.…”

Section: Introductionmentioning

confidence: 99%

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A MCS-RSM approach for network reliability to minimise the total cost

Yeh

2003

The International Journal of Advanced Manufacturing Technology

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Evaluating the system reliability of a stochastic network is an important topic in the planning, designing and control of systems. It is always desirable to minimise the resource consumption, e.g., the total cost, under the network reliability constraint in a real-life network problem. In this study, an intuitive Monte Carlo simulation (MCS) was first developed to find the estimated reliability w.r.t some specific combinations of the node reliability. Then, the response surface methodology (RSM) with the Box-Behnken design (BBD) was implemented to obtain the reliability function. Next, the proposed problem was modelled and solved by nonlinear programming. One example is given to illustrate the proposed MCS-RSM approach.Keywords Reliability AE Cost AE NP-hard AE Monte Carlo simulation (MCS) AE Response surface methodology (RSM) Notations Acronyms MCSMonte Carlo Simulation RSM Response Surface Methodology BBD Box-Behnken designed MC/MP minimal pathset/cutset Reliability optimisation has been a popular area of research and has received a significant amount of attention during the past four decades [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] due to reliability Int

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Section: Mcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A MCS-RSM approach for network reliability to minimise the total cost

Yeh

2003

The International Journal of Advanced Manufacturing Technology

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“…This phenomenon occurs in many real-world applications like transmission line networks, chemical processes, manufacturing industries, etc. In recent years, system availability has received extensive interest in many real-world systems such as computer [3] and communication systems [4], transporta-tion systems [5], oil/gas production systems [6], etc. Most of studies considered two states for each system, so that the system was either working, or it completely failed.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Multi-State Degraded System to Optimize Maintenance/Repair Costs and System Availability

et al. 2017

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Abstract. In this study, a multi-state degraded system is studied, where status of system is continuously degrading over time. As time progresses, system may either deteriorate gradually and go to lower performance state, or it may fail suddenly. If the system fails, some repairs are carried out to restore the system to the previous state. When the inspections reveal that the system has reached its last acceptable state, a PM is carried out to restore the system to the higher performance states. The goal is to nd the optimal PM level, so that the mean availability of the system is maximized and the total cost of the system is minimized. In this regard, Markov process is employed to represent di erent states of system. An integrated optimization approach is also suggested based on the desirability function of statistical approach. The suggested aggregation method is robust to the potential dependency between the total cost and the mean availability. It also ensures that both objective functions fall in decision-maker's acceptable region. In order to show the e ciency of the proposed approach, a numerical example is presented and analyzed.

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“…Reliability optimization problem has attracted significant attention in recent years due to the importance of reliability in various kinds of systems [2], [3], [4], [5], [6]. The network reliability optimization problem is to find a balance between cost and reliability.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization with Monte-Carlo simulation and hypothesis testing for network reliability problem

Chen

Deng

et al. 2016

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)

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Abstract-The performance of Monte-Carlo Simulation(MCS) is highly related to the number of simulation. This paper introduces a hypothesis testing technique and incorporated into a Particle Swarm Optimization(PSO) based Monte-Carlo Simulation(MCS) algorithm to solve the complex network reliability problem. The function of hypothesis testing technique is to reduce the dispensable simulation in network system reliability estimation. The proposed technique contains three components: hypothesis testing, network reliability calculation and PSO algorithm for finding solutions. The function of hypothesis testing is to abandon unpromising solutions; we use monte-carlo simulation to obtain network reliability; since the network reliability problem is NP-hard, PSO algorithm is applied. Since the execution time can be better decreased with the decrease of Confidence level of hypothesis testing in a range, but the solution becomes worse when the confidence level exceed a critical value, the experiment are carried out on different confidence levels for finding the critical value. The experimental results show that the proposed method can reduce the computational cost without any loss of its performance under a certain confidence level.

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An efficient algorithm for simultaneously deducing minimal paths as well as cuts of a communication network

Cited by 56 publications

References 9 publications

A MCS-RSM approach for network reliability to minimise the total cost

A MCS-RSM approach for network reliability to minimise the total cost

A Multi-Objective Multi-State Degraded System to Optimize Maintenance/Repair Costs and System Availability

Particle swarm optimization with Monte-Carlo simulation and hypothesis testing for network reliability problem

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