Paper Info
Title
Solving The Production Transportation Problem Via A Deterministic Annealing Neural Network Method
Abstract
The production transportation problem is a famous NP-hard problem which is a challenge to be solved. This study develops a deterministic annealing neural network method based on Lagrange-barrier functions and two neural network models to solve the problem of this kind. According to the problem's formulation, the Lagrange function will be applied to deal with the linear equality constraints. At the same time, the barrier function will be applied to make the solution arrive at the near-global or global optimal solution. For each of the two neural network models, an iterative procedure to optimize the proposed neural network will be developed and the descent direction is obtained. Then two Lyapunov functions corresponding to the two neural network models are proposed. On the basis of the Lyapunov functions, this deterministic annealing neural network method are shown to converge to the stable equilibrium state and be completely stable. Finally, preliminary numerical results on a number of test problems show that the developed method is promising and could be expanded to other similar issues in the real world. (C) 2021 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.amc.2021.126518
APPLIED MATHEMATICS AND COMPUTATION
Keywords
DocType
Volume
Neural network, Production transportation problem, Deterministic annealing, Combinatorial optimization, Lagrange function
Journal
411
ISSN
Citations 
PageRank 
0096-3003
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Zhengtian Wu1156.39
Qing Gao201.01
Baoping Jiang31157.12
H. R. Karimi43569223.59