Nonlinear neural network for deterministic scheduling

Conference · Fri Jan 01 00:00:00 EST 1988

OSTI ID:5152343

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

This paper addresses the NP-complete, deterministic scheduling problem for a single server system. 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 non-preemptive optimal processing schedule such that the total completion time, total tarediness and the number of tardy jobs is minimized. This model exhibits faster convergence than techniques based on gradient projection methods.

• Research Organization: Louisiana State Univ., Baton Rouge (USA). Dept. of Computer Science; Oak Ridge National Lab., TN (USA). Engineering Physics and Mathematics Div.
• DOE Contract Number: AC05-84OR21400
• OSTI ID: 5152343
• Report Number(s): CONF-8706130-5; ON: DE88008663
• Resource Relation: Conference: 1. IEEE international conference on neural networks, San Diego, CA, USA, 21 Jun 1987
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
TASK SCHEDULING
NONLINEAR PROBLEMS
ALGORITHMS
ARTIFICIAL INTELLIGENCE
DATA PROCESSING
MATHEMATICAL LOGIC
PROCESSING
990210* - Supercomputers- (1987-1989)