Neural network approach for multicriteria solid transportation problem

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.

[1]  Anthony G. Constantinides,et al.  Lagrange Neural Networks for Linear Programming , 1992, J. Parallel Distributed Comput..

[2]  Jun Wang Analogue neural network for solving the assignment problem , 1992 .

[3]  Mitsuo Gen,et al.  Neural network approach for allocation with capacity , 1996 .

[4]  Shengwei Zhang,et al.  Lagrange programming neural networks , 1992 .

[5]  Stefen Hui,et al.  Solving linear programming problems with neural networks: a comparative study , 1995, IEEE Trans. Neural Networks.

[6]  Hanif D. Sherali,et al.  Linear Programming and Network Flows , 1977 .

[7]  Chee-Kit Looi,et al.  Neural network methods in combinatorial optimization , 1992, Comput. Oper. Res..

[8]  Mitsuo Gen,et al.  Solving bicriteria solid transportation problem by genetic algorithm , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[9]  M. P. Biswal,et al.  Fuzzy programming approach to multiobjective solid transportation problem , 1993 .

[10]  Stefen Hui,et al.  On solving constrained optimization problems with neural networks: a penalty method approach , 1993, IEEE Trans. Neural Networks.

[11]  John R. Current,et al.  Multiobjective transportation network design and routing problems: Taxonomy and annotation , 1993 .

[12]  Jun Wang,et al.  Recurrent neural networks for linear programming: Analysis and design principles , 1992, Comput. Oper. Res..

[13]  Mitsuo Gen,et al.  A modified ANN for convex programming with linear constraints , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).