A Subjective Scheduler Based on Backpropagation Neural Network for Formulating a Real-life Scheduling Situation
This paper presents a subjective job scheduler based
on a 3-layer Backpropagation Neural Network (BPNN) and a greedy
alignment procedure in order formulates a real-life situation. The
BPNN estimates critical values of jobs based on the given subjective
criteria. The scheduler is formulated in such a way that, at each time
period, the most critical job is selected from the job queue and is
transferred into a single machine before the next periodic job arrives.
If the selected job is one of the oldest jobs in the queue and its
deadline is less than that of the arrival time of the current job, then
there is an update of the deadline of the job is assigned in order to
prevent the critical job from its elimination. The proposed
satisfiability criteria indicates that the satisfaction of the scheduler
with respect to performance of the BPNN, validity of the jobs and the
feasibility of the scheduler.
[1] W. Stinson, "An Introduction to the Design and Analysis of
Algorithms", Cambridge University press, 1980, pp.70-103.
[2] T. H. Cormen, C.E. Leiserson, R. L. Rivest and C. Stein, "Introduction
to Algorithms," The MIT Press: McGraw-Hill, 2001, pp. 370-399.
[3] K.G Anilkumar and T. Tanprasert, "Neural Network Based Priority
Assigner for Job Scheduler", AU Journal of Technology, AU J.T.9 (3)
2006, pp. 181-186.
[4] K.G. Anilkumar and T. Tanprasert, "Neural Network Based
Generalized Job-Shop Scheduler," in Proc. 2nd IMT-GT Regional
Conference on Mathematics, statistics and Applications, Universiti Sains
Malaysia, Penang, Malaysia, 2006, pp. 53 -58.
[5] K.G Anilkumar and T. Tanprasert, "Neural Network Bassed Greedy Job
Scheduler," in Proc. National Computer Science and Engineering
Conference (NCSEC 2006), Konkhean, Thailand, 2006, pp. 257-262.
[6] K. G. Anilkumar and T. Tanprasert, "Generalized Job-shop Scheduler
Using Feed Forward Neural network and Greedy Alignment Procedure,"
in Proc. IASTED Conference on Artificial Intelligence and
Applications, AIA-2007, Innsbruck, Austria, pp. 115-120.
[7] K. G Anilkumar and T. Tanprasert, "A Subjective Scheduler Based on
Neural Network for Job Routing in a Generalized Job-Shop Problem",
GESTS International Transactions on Computer Science and
Engineering, vol.45, 2008, pp. 79-96.
[8] V. B. Rao and H. V. Rao, "Neural Networks & Fuzzy logic," BPB
Publications, New Delhi, 1996, pp. 150-300.
[9] R. A. Johnson and D. W. Wichern, "Applied Multivariate Statistical
Analysis," 5th edition, NJ: Prentice Hall, NJ, 2002, pp. 668-719.
[1] W. Stinson, "An Introduction to the Design and Analysis of
Algorithms", Cambridge University press, 1980, pp.70-103.
[2] T. H. Cormen, C.E. Leiserson, R. L. Rivest and C. Stein, "Introduction
to Algorithms," The MIT Press: McGraw-Hill, 2001, pp. 370-399.
[3] K.G Anilkumar and T. Tanprasert, "Neural Network Based Priority
Assigner for Job Scheduler", AU Journal of Technology, AU J.T.9 (3)
2006, pp. 181-186.
[4] K.G. Anilkumar and T. Tanprasert, "Neural Network Based
Generalized Job-Shop Scheduler," in Proc. 2nd IMT-GT Regional
Conference on Mathematics, statistics and Applications, Universiti Sains
Malaysia, Penang, Malaysia, 2006, pp. 53 -58.
[5] K.G Anilkumar and T. Tanprasert, "Neural Network Bassed Greedy Job
Scheduler," in Proc. National Computer Science and Engineering
Conference (NCSEC 2006), Konkhean, Thailand, 2006, pp. 257-262.
[6] K. G. Anilkumar and T. Tanprasert, "Generalized Job-shop Scheduler
Using Feed Forward Neural network and Greedy Alignment Procedure,"
in Proc. IASTED Conference on Artificial Intelligence and
Applications, AIA-2007, Innsbruck, Austria, pp. 115-120.
[7] K. G Anilkumar and T. Tanprasert, "A Subjective Scheduler Based on
Neural Network for Job Routing in a Generalized Job-Shop Problem",
GESTS International Transactions on Computer Science and
Engineering, vol.45, 2008, pp. 79-96.
[8] V. B. Rao and H. V. Rao, "Neural Networks & Fuzzy logic," BPB
Publications, New Delhi, 1996, pp. 150-300.
[9] R. A. Johnson and D. W. Wichern, "Applied Multivariate Statistical
Analysis," 5th edition, NJ: Prentice Hall, NJ, 2002, pp. 668-719.
@article{"International Journal of Information, Control and Computer Sciences:59883", author = "K. G. Anilkumar and T. Tanprasert", title = "A Subjective Scheduler Based on Backpropagation Neural Network for Formulating a Real-life Scheduling Situation", abstract = "This paper presents a subjective job scheduler based
on a 3-layer Backpropagation Neural Network (BPNN) and a greedy
alignment procedure in order formulates a real-life situation. The
BPNN estimates critical values of jobs based on the given subjective
criteria. The scheduler is formulated in such a way that, at each time
period, the most critical job is selected from the job queue and is
transferred into a single machine before the next periodic job arrives.
If the selected job is one of the oldest jobs in the queue and its
deadline is less than that of the arrival time of the current job, then
there is an update of the deadline of the job is assigned in order to
prevent the critical job from its elimination. The proposed
satisfiability criteria indicates that the satisfaction of the scheduler
with respect to performance of the BPNN, validity of the jobs and the
feasibility of the scheduler.", keywords = "Backpropagation algorithm, Critical value, Greedy
alignment procedure, Neural network, Subjective criteria,
Satisfiability.", volume = "2", number = "9", pages = "3145-7", }