2002
DOI: 10.1007/3-540-36131-6_47
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An Efficient Neural Network Algorithm for the p-Median Problem

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Cited by 8 publications
(4 citation statements)
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“…Data are generally presented to the network through a set of input cells. After a processing phase, the result is available through a set of cells referred toas output cells [20]. These cells interact with the external world but other cells with no direct interaction with the external world can exist (they are referred to as hidden cells).…”
Section: The Topology Of the Networkmentioning
confidence: 99%
“…Data are generally presented to the network through a set of input cells. After a processing phase, the result is available through a set of cells referred toas output cells [20]. These cells interact with the external world but other cells with no direct interaction with the external world can exist (they are referred to as hidden cells).…”
Section: The Topology Of the Networkmentioning
confidence: 99%
“…And some approximation algorithms such as branch-and-bound, 12 branch-and-price, 13 Lagrangian relaxation, 14 integral programming, 15 linear programming relaxation, 16 and randmized rounding 17 are widely used to solve the p-center problem and no capacity limits facility location problems. Besides, the GA, 18 neural network, 19 and simulated annealing 20 are used to solve the large-scale p-center problem.…”
Section: Related Workmentioning
confidence: 99%
“…Each neuron i has an input h i (k) and an output S i (k). The proposed architecture has been used satisfactorily to solve other optimization problems as location problems [12]. In order to design a suitable neural network architecture for this problem, we define the input of each neuron and its dynamical rule.…”
Section: Neural Network Architecture and Computation Dynamicsmentioning
confidence: 99%
“…the active neuron. Dominguez and Mun˜oz [12] proposed a competitive dynamics using recurrent neural networks to solve traditional location problems. Applying a winnertake-all learning mechanism to a neural network is frequently referred to as a competitive neural network.…”
Section: Neural Network Architecture and Computation Dynamicsmentioning
confidence: 99%