InforLorV4, PascalFrancis, Corpus, bibRecord, 000D36

Anytime scheduling with neural networks

Identifieur interne : 000D36 ( PascalFrancis/Corpus ); précédent : 000D35; suivant : 000D37

Anytime scheduling with neural networks

Auteurs : J.-M. Gallone ; F. Charpillet ; F. Alexandre

Source :

RBID : Pascal:96-0350284

Descripteurs français

Pascal (Inist)
- Ordonnancement, Réseau neuronal, Modèle Hopfield, Complexité calcul, Problème NP dur, Solution optimale, Système informatique, Anytime algorithm.

English descriptors

KwdEn :
- Computational complexity, Computer system, Hopfield model, NP hard problem, Neural network, Optimal solution, Scheduling.

Abstract

Scheduling techniques have been intensively studied by several research communities and have been applied to a wide range of applications in computer and manufacturing environments. In computer systems, scheduling is an important approach to address real-time constraints associated with a set of computing tasks to be executed on one or several computers. Most of the scheduling problems are NP-Hard, which is why heuristic and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions with a polynomial computational complexity. This paper presents results for scheduling a set of non preemptive tasks by using a Hopfield neural network model. We present in this paper how this approach can solve scheduling problems following the "anytime" paradigm.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A08	`01`	`1`	`ENG`	`@1 Anytime scheduling with neural networks`
A09	`01`	`1`	`ENG`	`@1 ETFA '95 : 1995 INRIA/IEEE symposium on emerging technologies and factory automation`
A11	`01`	`1`		`@1 GALLONE (J.-M.)`
A11	`02`	`1`		`@1 CHARPILLET (F.)`
A11	`03`	`1`		`@1 ALEXANDRE (F.)`
A14	`01`			`@1 CRIN-CNRS & INRIA Lorraine, BP 239 @2 54506 Vandoœuvre-lès-Nancy @3 FRA @Z 1 aut. @Z 2 aut. @Z 3 aut.`
A18	`01`	`1`		`@1 INRIA. Centre de Rocquencourt @2 Rocquencourt @3 FRA @9 patr.`
A18	`02`	`1`		`@1 IEEE. Industrial Electronics Society @3 USA @9 patr.`
A20				`@1 509-520`
A21				`@1 1995`
A23	`01`			`@0 ENG`
A25	`01`			`@1 IEEE Computer Society Press @2 Los Alamitos CA`
A30	`01`	`1`	`ENG`	`@1 INRIA / IEEE symposium on emerging technologies and factories automation @3 Paris FRA @4 1995-10-10`
A43	`01`			`@1 INIST @2 Y 31160 @5 354000043171200450`
A44				`@0 0000 @1 © 1996 INIST-CNRS. All rights reserved.`
A45				`@0 12 ref.`
A47	`01`	`1`		`@0 96-0350284`
A60				`@1 C`
A61				`@0 A`
A66	`01`			`@0 USA`
C01	`01`		`ENG`	@0 Scheduling techniques have been intensively studied by several research communities and have been applied to a wide range of applications in computer and manufacturing environments. In computer systems, scheduling is an important approach to address real-time constraints associated with a set of computing tasks to be executed on one or several computers. Most of the scheduling problems are NP-Hard, which is why heuristic and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions with a polynomial computational complexity. This paper presents results for scheduling a set of non preemptive tasks by using a Hopfield neural network model. We present in this paper how this approach can solve scheduling problems following the "anytime" paradigm.
C02	`01`	`X`		`@0 001D02C06`
C02	`02`	`X`		`@0 001D02B04`
C02	`03`	`X`		`@0 001D02A05`
C03	`01`	`X`	`FRE`	`@0 Ordonnancement @5 01`
C03	`01`	`X`	`ENG`	`@0 Scheduling @5 01`
C03	`01`	`X`	`GER`	`@0 Netzplantechnik @5 01`
C03	`01`	`X`	`SPA`	`@0 Ordonamiento @5 01`
C03	`02`	`X`	`FRE`	`@0 Réseau neuronal @5 02`
C03	`02`	`X`	`ENG`	`@0 Neural network @5 02`
C03	`02`	`X`	`SPA`	`@0 Red neuronal @5 02`
C03	`03`	`X`	`FRE`	`@0 Modèle Hopfield @5 03`
C03	`03`	`X`	`ENG`	`@0 Hopfield model @5 03`
C03	`03`	`X`	`SPA`	`@0 Modelo Hopfield @5 03`
C03	`04`	`X`	`FRE`	`@0 Complexité calcul @5 04`
C03	`04`	`X`	`ENG`	`@0 Computational complexity @5 04`
C03	`04`	`X`	`SPA`	`@0 Complejidad computación @5 04`
C03	`05`	`X`	`FRE`	`@0 Problème NP dur @5 05`
C03	`05`	`X`	`ENG`	`@0 NP hard problem @5 05`
C03	`05`	`X`	`SPA`	`@0 Problema NP duro @5 05`
C03	`06`	`X`	`FRE`	`@0 Solution optimale @5 06`
C03	`06`	`X`	`ENG`	`@0 Optimal solution @5 06`
C03	`06`	`X`	`SPA`	`@0 Solución óptima @5 06`
C03	`07`	`X`	`FRE`	`@0 Système informatique @5 07`
C03	`07`	`X`	`ENG`	`@0 Computer system @5 07`
C03	`07`	`X`	`SPA`	`@0 Sistema informático @5 07`
C03	`08`	`X`	`FRE`	`@0 Anytime algorithm @4 INC @5 72`
N21				`@1 246`

Format Inist (serveur)

NO :	PASCAL 96-0350284 INIST
ET :	Anytime scheduling with neural networks
AU :	GALLONE (J.-M.); CHARPILLET (F.); ALEXANDRE (F.)
AF :	CRIN-CNRS & INRIA Lorraine, BP 239/54506 Vandoœuvre-lès-Nancy/France (1 aut., 2 aut., 3 aut.)
DT :	Congrès; Niveau analytique
SO :	INRIA / IEEE symposium on emerging technologies and factories automation/1995-10-10/Paris FRA; Etats-Unis; Los Alamitos CA: IEEE Computer Society Press; Da. 1995; Pp. 509-520
LA :	Anglais
EA :	Scheduling techniques have been intensively studied by several research communities and have been applied to a wide range of applications in computer and manufacturing environments. In computer systems, scheduling is an important approach to address real-time constraints associated with a set of computing tasks to be executed on one or several computers. Most of the scheduling problems are NP-Hard, which is why heuristic and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions with a polynomial computational complexity. This paper presents results for scheduling a set of non preemptive tasks by using a Hopfield neural network model. We present in this paper how this approach can solve scheduling problems following the "anytime" paradigm.
CC :	001D02C06; 001D02B04; 001D02A05
FD :	Ordonnancement; Réseau neuronal; Modèle Hopfield; Complexité calcul; Problème NP dur; Solution optimale; Système informatique; Anytime algorithm
ED :	Scheduling; Neural network; Hopfield model; Computational complexity; NP hard problem; Optimal solution; Computer system
GD :	Netzplantechnik
SD :	Ordonamiento; Red neuronal; Modelo Hopfield; Complejidad computación; Problema NP duro; Solución óptima; Sistema informático
LO :	INIST-Y 31160.354000043171200450
ID :	96-0350284

Links to Exploration step

Pascal:96-0350284

Le document en format XML

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Anytime scheduling with neural networks

Anytime scheduling with neural networks

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Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

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