A New Method for the Minimum Concave Cost Transportation Problem in Smart Transportation
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.
KeywordsMinimum concave cost transportation problem Neural network algorithm Neural network models Lagrange-barrier functions
This work was partially supported by NSFC under Grant No. 61672371, GRF: CityU 11302715 of Hong Kong SAR Government, Jiangsu Provincial Department of Housing and Urban–Rural Development under grants No. 2017ZD253, Ministry of housing and urban and rural construction under grants No. 2018-K1-007, the grant from Suzhou University of Science and Technology under grants No. XKZ2017011, and China scholarship council.
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