NONLINEAR NEURAL NETWORKS FOR DETERMINISTIC SCHEDULING.

S. Gulati; S. S. Iyengar; N. Toomarian; V. Protopopescu; J. Barhen

NONLINEAR NEURAL NETWORKS FOR DETERMINISTIC SCHEDULING.

S. Gulati, S. S. Iyengar, N. Toomarian, V. Protopopescu, J. Barhen

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

14 Scopus citations

Abstract

The NP-complete, deterministic scheduling problem for a single server system is addressed. Given a set of n tasks along with the precedence constraints among them, their timing requirements, setup costs, and their completion deadlines, a neuromorphic model is used to construct a nonpreemptive optimal processing schedule so that the total completion time, total tardiness, and the number of tardy jobs are minimized. This model exhibits faster convergence than techniques based on gradient projection methods.

Original language	English
Pages	iv/745-752
State	Published - 1987
Externally published	Yes

Cite this

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title = "NONLINEAR NEURAL NETWORKS FOR DETERMINISTIC SCHEDULING.",

abstract = "The NP-complete, deterministic scheduling problem for a single server system is addressed. Given a set of n tasks along with the precedence constraints among them, their timing requirements, setup costs, and their completion deadlines, a neuromorphic model is used to construct a nonpreemptive optimal processing schedule so that the total completion time, total tardiness, and the number of tardy jobs are minimized. This model exhibits faster convergence than techniques based on gradient projection methods.",

author = "S. Gulati and Iyengar, {S. S.} and N. Toomarian and V. Protopopescu and J. Barhen",

year = "1987",

language = "English",

pages = "iv/745--752",

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TY - CONF

T1 - NONLINEAR NEURAL NETWORKS FOR DETERMINISTIC SCHEDULING.

AU - Gulati, S.

AU - Iyengar, S. S.

AU - Toomarian, N.

AU - Protopopescu, V.

AU - Barhen, J.

PY - 1987

Y1 - 1987

N2 - The NP-complete, deterministic scheduling problem for a single server system is addressed. Given a set of n tasks along with the precedence constraints among them, their timing requirements, setup costs, and their completion deadlines, a neuromorphic model is used to construct a nonpreemptive optimal processing schedule so that the total completion time, total tardiness, and the number of tardy jobs are minimized. This model exhibits faster convergence than techniques based on gradient projection methods.

AB - The NP-complete, deterministic scheduling problem for a single server system is addressed. Given a set of n tasks along with the precedence constraints among them, their timing requirements, setup costs, and their completion deadlines, a neuromorphic model is used to construct a nonpreemptive optimal processing schedule so that the total completion time, total tardiness, and the number of tardy jobs are minimized. This model exhibits faster convergence than techniques based on gradient projection methods.

UR - http://www.scopus.com/inward/record.url?scp=0023541064&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0023541064

SP - iv/745-752

ER -

NONLINEAR NEURAL NETWORKS FOR DETERMINISTIC SCHEDULING.

Abstract

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