Cooperative Strategies for Maximum-Flow Problem in Uncertain Decentralized Systems Using Reliability Analysis

gharehbolagh, hadi heidari; Hafezalkotob, Ashkan; Makui, Ahmad; Raissi, Sadigh

doi:10.1155/2016/2349712

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…In financial networks, Eboli [17] has described the balance-sheet as a loss flow on the arc. e time-varying maximum flow problem (TMFP) [18,19] and wastage maximum flow problem (WMFP) [20] have been investigated extensively. Li and Buzdalov et al [21,22] proposed their algorithms for solving the TMFPs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Maximum Flow Algorithm with Neural Network for Time-Varying Wastage Networks

Zhang

Jiang

Huang

2022

Security and Communication Networks

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This paper introduces a time-varying wastage maximum flow problem (TWMFP) and proposes a time-flow neural network (TFNN) for solving the TWMFPs. The time-varying wastage maximum flow problem is concerned with finding the maximum flow in a network with time-varying arc capacities and additive flow losses on the arcs. This problem has multiple applications in transportation, communication, and financial network. For example, solving the maximum traffic flow of the transportation network and the maximum profit of the financial network. Unlike traditional neural network algorithms, the proposed TFNN does not require any training by means of its time-flow mechanism. The time-flow mechanism is realized by each active neuron sending pulses to its successor neurons. In order to maximize the network flow, the proposed TFNN can be divided into two parts: path-pulse neural networks (PPNNs) and subnet-flow neural networks (SFNN). PPNN is to generate two subnet sets (viz. with wastage arcs and without), and SFNN is to find the maximum flow value of each subnet. The subnet computing strategy of the proposed algorithm greatly improves the solution accuracy of TWMFPs. Theoretical analysis and experiments have proved the effectiveness of TFNN. The experiment results of the transportation network (viz. New York Road) show that the proposed TFNN has better performance (viz. error rate and computational time) compared to classical algorithms.

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Section: Introductionmentioning

confidence: 99%

A Novel Maximum Flow Algorithm with Neural Network for Time-Varying Wastage Networks

Zhang

Jiang

Huang

2022

Security and Communication Networks

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“…Their research demonstrated that powerful maximum flow algorithm can be developed using borrowed or new techniques, thus departing from finding maximum flow using augmenting path and residual path concepts. Gharehbolagh et al (2016) proposed a maximum flow optimization model that incorporated reliability analysis. Application of reliability in maximum flow networks helps the decision makers to make better decisions.…”

Section: Motivationmentioning

confidence: 99%

Extreme Min – Cut Max – Flow Algorithm

Tawanda

Nyamugure

Munapo

et al. 2023

International Journal of Applied Metaheuristic Computing

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In this article, the authors propose a maximum flow algorithm based on flow matrix. The algorithm only requires the effort to reduce the capacity of the underutilized arcs to that of the respective flow. The optimality of the algorithm is proved by the max-flow min-cut theorem. The algorithm is table-based, thus avoiding augmenting path and residual network concepts. The authors used numerical examples and computational comparisons to demonstrate the efficiency of the algorithm. These examples and comparisons revealed that the proposed algorithm is capable of computing exact solutions while using few iterations as compared to some existing algorithms.

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“…Heidari Gharehbolagh et al (2016) proposed a mathematical programming model based on triangular reliability function to maintain reliability of flow in decentralized networks to examine how self-governing owners can cooperate with each other.…”

Section: Reliability Analysismentioning

confidence: 99%

A cooperative game approach to uncertain decentralized logistic systems subject to network reliability considerations

gharehbolagh

Hafezalkotob

Makui

et al. 2017

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Purpose Maximum-flow of an uncertain multi-owner network has become very important recently. This study aims to evaluate the maximum flow on a cooperated logistic system in the presence of uncertainties, raised by travel time, capacity, cost and failures. Design/methodology/approach To consider different uncertainties and to promote network efficiency, the proposed model is enriched with a cooperative game methodology and a reliability method. A scenario-based method covers optimistic, pessimistic and most likely estimates time, cost and capacity of each route as well as applies a prior failure pattern for breakdown of any resource. Findings A linear optimization model, which is enriched with target reliability estimation, is presented. Results on a water distribution network indicate more revenue performance for players. Carrying out sensitivity analysis shows the importance of the model parameters. Originality/value Modeling maximum-flow problem in the presence of many sources of uncertainty with the aim of a cooperative game is the main contribution of the present study. Also, a novel method based on the reliability theory is applied to close the chasm on evaluating the real maximum flow in a shared decentralized network which suffers from risky conditions on arcs and nodes.

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Cooperative Strategies for Maximum-Flow Problem in Uncertain Decentralized Systems Using Reliability Analysis

Cited by 5 publications

References 34 publications

A Novel Maximum Flow Algorithm with Neural Network for Time-Varying Wastage Networks

A Novel Maximum Flow Algorithm with Neural Network for Time-Varying Wastage Networks

Extreme Min – Cut Max – Flow Algorithm

A cooperative game approach to uncertain decentralized logistic systems subject to network reliability considerations

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