A neural network job-shop scheduler |
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Authors: | Gary R. Weckman Chandrasekhar V. Ganduri David A. Koonce |
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Affiliation: | (1) Department of Industrial and Systems Engineering, Ohio University, 280, Stocker Center, Athens, OH 45701, USA;(2) Department of Industrial and Systems Engineering, Ohio University, 279, Stocker Center, Athens, OH 45701, USA;(3) Department of Industrial and Systems Engineering, Ohio University, 283, Stocker Center, Athens, OH 45701, USA |
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Abstract: | This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NN scheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA. |
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Keywords: | Artificial neural networks Scheduling Job-shop Machine learning Genetic algorithms |
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