TY - JFULL AU - K. G. Anilkumar and T. Tanprasert PY - 2008/10/ TI - A Subjective Scheduler Based on Backpropagation Neural Network for Formulating a Real-life Scheduling Situation T2 - International Journal of Computer and Information Engineering SP - 3150 EP - 3157 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/10830 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 21, 2008 N2 - 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. ER -