A simple algorithm for reliability evaluation of a stochastic-flow network with node failure

Lin, Yi‐Kuei

doi:10.1016/s0305-0548(00)00039-3

Cited by 242 publications

(158 citation statements)

References 12 publications

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“…However, in real life, the arc capacities of networks are often random due to the uncertain events such as accidents, maintenances, capacity failures, etc. 11 . When dealing with the network with uncertain arc capacities, how to compute its capacity is a complex problem and always appeal to the TTR index 11 , which has been discussed elaborately in terms of various methods [12][13] .…”

Section: General Statementsmentioning

confidence: 99%

“…11 . When dealing with the network with uncertain arc capacities, how to compute its capacity is a complex problem and always appeal to the TTR index 11 , which has been discussed elaborately in terms of various methods [12][13] . Moreover, the network capacity computation with constraint of TTR has been proved to be a NP-hard problem 14 .…”

Section: General Statementsmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

Wang¹,

Li²,

Wang³

et al. 2011

IJCIS

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Motivated by a problem in the commodity transportation, a mathematical model is developed to calculate capacity of single-commodity network when the time reliability levels of transporting commodity between origin-destination pairs are constrained. We use a hybrid intelligent algorithm, in which genetic algorithm is embedded with Monte Carlo simulation to solve the optimization model. In the hybrid intelligent algorithm, the genetic algorithm is used to report the best path flow solutions and the Monte Carlo simulation is to check the feasibility of the chromosomes of genetic algorithm. With a computational experiment, the fact that network capacity decreases with the increase of the transportation time reliability level is validated. The efficacies of the developed procedures are examined by comparing the computational times of solving algorithm with that of previous work.

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Section: General Statementsmentioning

confidence: 99%

Section: General Statementsmentioning

confidence: 99%

Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

Wang¹,

Li²,

Wang³

et al. 2011

IJCIS

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show abstract

“…The algorithm of Lin (2001) introduced the method used to compute minimal path sets of binary-state systems to multistate systems. However, they limited the states of system components to be weakly homogeneous.…”

Section: Literature Reviewmentioning

confidence: 99%

3.2.4 Study on the Influence of Component Uncertainty on Reliability Estimation of Multi‐State Systems with Continuous States

Zhang

Mostashari

2009

INCOSE International Symp

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Abstract. Existing reliability evaluation methods rely on the availability of accurate component states data. They will become ineffective when the states themselves are uncertain or unknown, which usually happens during the early stages of the development of new systems. In such cases it is important to understand how uncertainties will affect the system reliability measures. Another drawback of current methods studying reliability of Multi-State System (MSS) is that they only considered the systems whose components have several discrete states. For those whose components have continuous states, these methods are not effective either. This paper considered the continuous distribution of components states during the approximation of Multi-State System (MSS) reliability and proposed a method to assess the reliability of this kind of system using Monte-Carlo simulation. This method will also be useful when we have no enough data to know the exact discrete states and related probability, and can only estimate components states distribution types and related parameters. Two examples were employed to illustrate the method. Comparison of the two examples shows that component state uncertainty has significant influence on the assessment of system reliability. Our effort will make the reliability approximation more realistic compared with existing methods.

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“…Alternative objectives that consider these and other issues of equity that arise in solutions for the evacuation problem have been proposed in the literature (e.g. [19,27]). Regardless of the objective that is chosen for the determination of the optimal instructions, the issue of shared information arises if instructions are to be provided by changeable message signs or comparable technologies that inform more than one person at a time.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

The building evacuation problem with shared information

Chen

Miller-Hooks

2008

Naval Research Logistics

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Abstract:In this article, the Building Evacuation Problem with Shared Information (BEPSI) is formulated as a mixed integer linear program, where the objective is to determine the set of routes along which to send evacuees (supply) from multiple locations throughout a building (sources) to the exits (sinks) such that the total time until all evacuees reach the exits is minimized. The formulation explicitly incorporates the constraints of shared information in providing online instructions to evacuees, ensuring that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. Arc travel time and capacity, as well as supply at the nodes, are permitted to vary with time and capacity is assumed to be recaptured over time. The BEPSI is shown to be NP-hard. An exact technique based on Benders decomposition is proposed for its solution. Computational results from numerical experiments on a real-world network representing a four-story building are given. Results of experiments employing Benders cuts generated in solving a given problem instance as initial cuts in addressing an updated problem instance are also provided.

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A simple algorithm for reliability evaluation of a stochastic-flow network with node failure

Cited by 242 publications

References 12 publications

Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints

3.2.4 Study on the Influence of Component Uncertainty on Reliability Estimation of Multi‐State Systems with Continuous States

The building evacuation problem with shared information

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