Complex Scheduling with Potts Neural Networks
(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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https://lup.lub.lu.se/record/a32e5b8b-cf11-4aa8-a9dc-abdcaffa8442
- author
- Gislén, Lars LU ; Peterson, Carsten LU and Söderberg, Bo LU
- organization
- publishing date
- 1992
- 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}}, }