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A new method for the minimum concave cost transportation problem in smart transportation

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Abstract

    The minimum concave cost transportation problem is the benchmark problem in numerical computing and it has been used widely in the schedule of smart transportation. In this paper, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. The algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints and the barrier function is used to force the solution to move to the global or near-global optimal solution. The computer simulations on test problem are made and the results indicate that the proposed algorithm always generates global or near global optimal solutions.
    Original languageEnglish
    Title of host publicationAdvancements in Smart City and Intelligent Building - Proceedings of the International Conference on Smart City and Intelligent Building (ICSCIB 2018)
    EditorsQiansheng Fang, Quanmin Zhu, Feng Qiao
    PublisherSpringer Singapore
    Pages363-369
    ISBN (Electronic)978-981-13-6733-5
    ISBN (Print)978-981-13-6732-8
    DOIs
    Publication statusPublished - Sept 2018
    EventInternational Conference on Smart City and Intelligent Building, ICSCIB 2018 - Hefei, China
    Duration: 15 Sept 201816 Sept 2018

    Publication series

    NameAdvances in Intelligent Systems and Computing
    Volume890
    ISSN (Print)2194-5357

    Conference

    ConferenceInternational Conference on Smart City and Intelligent Building, ICSCIB 2018
    PlaceChina
    CityHefei
    Period15/09/1816/09/18

    Research Keywords

    • Lagrange-barrier functions
    • Minimum concave cost transportation problem
    • Neural network algorithm
    • Neural network models

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