Scheduling a Job-shop Using a Modified Back- Error Propagation Neural Network1
نویسندگان
چکیده
General job-shop scheduling is a difficult problem to be solved. The complexity of the problem is highlighted by the fact that in a general job-shop having m machines and n jobs the total number of schedules can be as high as (n!), hence if “n=20 m=10” the number of possible solutions is 7.2651x10. Many approaches such as Branch and Bound, Simulated Annealing, Tabu Search and others have been tried but with limited success. Recent advances in neural technology led the way to use neural networks to solve this problem. Here we present a work based on training a modified back-error propagation network to solve the job-shop scheduling problem after reviewing major neural network based job-shop scheduling systems.
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