A neural network for dispatching rule selection in a job shop |
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Authors: | Ahmed El-Bouri Pramit Shah |
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Affiliation: | (1) Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria, M5B 2K3 Toronto, Ontario, Canada |
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Abstract: | This paper investigates an intelligent system that selects dispatching rules to apply locally for each machine in a job shop. Randomly generated problems are scheduled using optimal permutations of three different dispatching rules on five machines. A neural network is then trained to associate between a statistical characterization of the job mix in each of these problems, with the best combination of dispatching rules to use. Once trained, the neural network is able to recommend for new problems a dispatching rule to use on each machine. Two networks are trained separately for minimizing makespan and the mean flowtime in the job shop. Test results show that the combinations of dispatching rules suggested by the trained networks produce better results, for both objectives, than the alternative of using a single rule common to all machines. |
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Keywords: | Job shop Neural networks Dispatching rules Makespan Mean flowtime |
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