This paper aims to present and discuss the concept of a subjective job scheduler based on a Backpropagation Neural Network (BPNN) and a greedy job alignment procedure. The subjective criteria of the scheduler depend on the solution plan for a given job scheduling problem. When the scheduler is provided with desired job selection criteria for the problem, it generates user satisfying solution from a set of valid jobs. The job validation procedure is based on the similarity measure of the jobs with the seen dataset of the scheduler. The seen dataset is based on the subjective criteria of the scheduler. The prioritized and valid jobs are allowed to execute concurrently on the given identical machines. The satisfying criterion of the scheduler indicates the user satisfaction of the scheduler and is based on three measures: convergence test of the BPNN, job validity test and cost evaluation. The simulations presented in this paper indicate that the proposed scheduler approach is one of the most effective strategies of structuring a subjective job scheduler.
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