A novel Hopfield neural network approach for minimizing total weighted tardiness of jobs scheduled on identical machines

This paper explores fast, polynomial time heuristic approximate solutions to the NP-hard problem of scheduling jobs on N identical machines. The jobs are independent and are allowed to be stopped and restarted on another machine at a later time. They have well-defined deadlines, and relative priorit...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2012-07
Main Authors: Fogarasi, N, Tornai, K, Levendovszky, J
Format: Article
Language:English
Subjects:
Computational grids
Computer simulation
Computing costs
Heuristic
Heuristic methods
Neural networks
Organic chemistry
Polynomials
Production scheduling
Quadratic forms
Schedules
Scheduling
Sequential scheduling
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper explores fast, polynomial time heuristic approximate solutions to the NP-hard problem of scheduling jobs on N identical machines. The jobs are independent and are allowed to be stopped and restarted on another machine at a later time. They have well-defined deadlines, and relative priorities quantified by non-negative real weights. The objective is to find schedules which minimize the total weighted tardiness (TWT) of all jobs. We show how this problem can be mapped into quadratic form and present a polynomial time heuristic solution based on the Hopfield Neural Network (HNN) approach. It is demonstrated, through the results of extensive numerical simulations, that this solution outperforms other popular heuristic methods. The proposed heuristic is both theoretically and empirically shown to be scalable to large problem sizes (over 100 jobs to be scheduled), which makes it applicable to grid computing scheduling, arising in fields such as computational biology, chemistry and finance.
ISSN:2331-8422