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A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

Ahmed Y. Hamed1, Monagi H. Alkinani2, M. R. Hassan3, *
1 Department of Computer Science, Faculty of Computers and Information, Sohag University, Sohag, 82524, Egypt.
2 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia.
3 Department of Mathematics and Computer Science, Faculty of Science, Aswan University, Aswan, 81528, Egypt.
* Corresponding Author: M. R. Hassan. Email: .

Computers, Materials & Continua 2020, 64(3), 1579-1586. https://doi.org/10.32604/cmc.2020.010881

Received 03 April 2020; Accepted 06 May 2020; Issue published 30 June 2020

Abstract

Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations—i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper. The problem is formulated mathematically, and a genetic algorithm is proposed to determine the optimal solution. The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity. Some numerical examples are presented to illustrate the efficiency of the proposed approach.

Keywords

Flow network, capacity assignment, network reliability, genetic algorithms.

Cite This Article

A. Y. Hamed, M. H. Alkinani and M. R. Hassan, "A genetic algorithm to solve capacity assignment problem in a flow network," Computers, Materials & Continua, vol. 64, no.3, pp. 1579–1586, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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