Maximum Flow by Network Reconstruction Method

Munapo, Elias; Tawanda, Trust; Nyamugure, Philimon; Kumar, Santosh

doi:10.1007/978-3-031-19958-5_87

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The results revealed that the model is sometimes underestimated due to demand peak times. Munapo et al [6] determined the maximum flow using the network reconstruction method, the algorithm identified the outmost path and remove it in such a way that the original network maximum flow value is not affected. The algorithm is simple and computes exact solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Network Reconstruction Algorithm to Compute Maximum Flow for Water Supply Network: A Case Study of the City of Bulawayo, Zimbabwe

Tawanda,

Kumar,

Munapo

et al. 2024

Contemp. Math.

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Determining the maximum flow value in Water Supply Networks (WSN) is a common problem that is being faced by many cities during and after designing of WSN. In this article, the network reconstruction (NR) algorithm is applied to compute the maximum flow value for the city’s WSN based on the actual data. The city of Bulawayo has been selected for the following reasons, availability of research data, the city is facing severe water shedding and several studies have focused only on other issues such as alternative water sources, leakage detection and demand forecasting. The goal of this study is to determine the maximum flow value from the sources to the reservoirs. The results have revealed that the computed maximum flow value is within the estimated range of 110,000 cubic meters to 190,000 cubic meters. Dam sensitivity analysis were considered to determine the sources to give maintenance priority before the rain season. Several recommendations were suggested to improve the water supply situation.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Network Reconstruction Algorithm to Compute Maximum Flow for Water Supply Network: A Case Study of the City of Bulawayo, Zimbabwe

Tawanda,

Kumar,

Munapo

et al. 2024

Contemp. Math.

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“…A road network in New York was used to prove the validity and efficiency of the available flow neural network method. Many scientists, mathematicians, engineers, and computer scholars have proposed different methods to solve the maximal flow problem and related variants, such as maximum flow problem instance space analysis (Alipour et al, 2023), network reconstruction method for maximum flow problem (Munapo et al, 2022), poly-logarithmic maximum flows (Cen et al, 2023), maximum flow routing strategy (Yang et al, 2023), maximum flow in fuzzy environments (Bavandi & Bigdeli, 2023), and multicommodity flow problem (Gupta et al, 2023).…”

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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