Neural network parallel algorithm for meeting schedule problems

Kazuhiro Tsuchiya; Yoshiyasu Takefuji; Ken ichi Kurotani; Kunio Wakahara

Neural network parallel algorithm for meeting schedule problems

Kazuhiro Tsuchiya, Yoshiyasu Takefuji, Ken ichi Kurotani, Kunio Wakahara

Research output: Contribution to conference › Paper › peer-review

Abstract

A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M×S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M×P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions and that the solution quality of the proposed algorithm does not degrade with the problem size.

Original language	English
Pages	173-177
Number of pages	5
Publication status	Published - 1996 Dec 1
Externally published	Yes
Event	Proceedings of the 1996 IEEE Region 10 TENCON - Digital Signal Processing Applications Conference. Part 2 (of 2) - Perth, Aust Duration: 1996 Nov 26 → 1996 Nov 29

Other

Other	Proceedings of the 1996 IEEE Region 10 TENCON - Digital Signal Processing Applications Conference. Part 2 (of 2)
City	Perth, Aust
Period	96/11/26 → 96/11/29

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Cite this

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title = "Neural network parallel algorithm for meeting schedule problems",

abstract = "A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M×S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M×P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions and that the solution quality of the proposed algorithm does not degrade with the problem size.",

author = "Kazuhiro Tsuchiya and Yoshiyasu Takefuji and Kurotani, {Ken ichi} and Kunio Wakahara",

year = "1996",

month = dec,

day = "1",

language = "English",

pages = "173--177",

note = "Proceedings of the 1996 IEEE Region 10 TENCON - Digital Signal Processing Applications Conference. Part 2 (of 2) ; Conference date: 26-11-1996 Through 29-11-1996",

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T1 - Neural network parallel algorithm for meeting schedule problems

AU - Tsuchiya, Kazuhiro

AU - Takefuji, Yoshiyasu

AU - Kurotani, Ken ichi

AU - Wakahara, Kunio

PY - 1996/12/1

Y1 - 1996/12/1

N2 - A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M×S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M×P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions and that the solution quality of the proposed algorithm does not degrade with the problem size.

AB - A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M×S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M×P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions and that the solution quality of the proposed algorithm does not degrade with the problem size.

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EP - 177

T2 - Proceedings of the 1996 IEEE Region 10 TENCON - Digital Signal Processing Applications Conference. Part 2 (of 2)

Y2 - 26 November 1996 through 29 November 1996

ER -

Neural network parallel algorithm for meeting schedule problems

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