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An intelligent node labelling maximum flow algorithm

Author

Listed:
  • Trust Tawanda

    (National University of Science and Technology)

  • Philimon Nyamugure

    (National University of Science and Technology)

  • Santosh Kumar

    (RMIT University)

  • Elias Munapo

    (North West University)

Abstract

A network based intelligent node labelling (INL) algorithm for solving the maximal flow problem in directed networks is presented. The INL algorithm is intelligent in that it does not make use of augmenting path methodology to compute the maximal flow value. The principle of the INL algorithm is to design an optimal network by balancing the total inflow value with the total outflow value at all intermediate nodes, thus eliminating the excess or stagnant flow and reduction of underutilized outflow arcs. The INL algorithm makes use of at most two iterations to transform the initial maximal flow network with N-nodes into an optimal network with O(VE) worst- case time complexity. Two numerical illustrations were used to demonstrate how the INL algorithm determines the maximal flow value. Computational experiments were considered on three well studied, small instances in the literature. The proposed INL algorithm was compared to other seven existing algorithms in terms of optimal solution and number of iterations.

Suggested Citation

  • Trust Tawanda & Philimon Nyamugure & Santosh Kumar & Elias Munapo, 2023. "An intelligent node labelling maximum flow algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1276-1284, August.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:4:d:10.1007_s13198-023-01930-3
    DOI: 10.1007/s13198-023-01930-3
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    References listed on IDEAS

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    1. Zhao, Peixin & Han, Xue & Wan, Di, 2021. "Evaluation of the airport ferry vehicle scheduling based on network maximum flow model," Omega, Elsevier, vol. 99(C).
    2. Hafezalkotob, Ashkan & Makui, Ahmad, 2015. "Cooperative maximum-flow problem under uncertainty in logistic networks," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 593-604.
    3. ÇalIskan, Cenk, 2011. "A specialized network simplex algorithm for the constrained maximum flow problem," European Journal of Operational Research, Elsevier, vol. 210(2), pages 137-147, April.
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