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Complex Scheduling with Potts Neural Networks

Gislén, Lars LU ; Peterson, Carsten LU and Söderberg, Bo LU (1992) In Neural Computation 4(6). p.805-831
Abstract
In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved... (More)
In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results. (Less)
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Neural Computation
volume
4
issue
6
pages
805 - 831
publisher
MIT Press
ISSN
1530-888X
DOI
10.1162/neco.1992.4.6.805
language
English
LU publication?
yes
id
a32e5b8b-cf11-4aa8-a9dc-abdcaffa8442
date added to LUP
2019-05-13 19:42:42
date last changed
2021-08-25 08:34:13
@article{a32e5b8b-cf11-4aa8-a9dc-abdcaffa8442,
  abstract     = {{In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results.}},
  author       = {{Gislén, Lars and Peterson, Carsten and Söderberg, Bo}},
  issn         = {{1530-888X}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{805--831}},
  publisher    = {{MIT Press}},
  series       = {{Neural Computation}},
  title        = {{Complex Scheduling with Potts Neural Networks}},
  url          = {{http://dx.doi.org/10.1162/neco.1992.4.6.805}},
  doi          = {{10.1162/neco.1992.4.6.805}},
  volume       = {{4}},
  year         = {{1992}},
}