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.


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    Title :

    A New Method for the Minimum Concave Cost Transportation Problem in Smart Transportation


    Contributors:
    Li, Chuan (author) / Wu, Zhengtian (author) / Fu, Baochuan (author) / Dang, Chuangyin (author) / Zheng, Jinjin (author)


    Publication date :

    2019-01-01


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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