Abstract
Scheduling of re-entrant lines is very important for manufacturing systems. For some dynamic scheduling methodologies, it is necessary to model a production system with finite-state discrete-time Markov process. However, proper state cannot be found as absorbing state of Markov process when general Mean Output Rate is employed as an evaluating criterion. Mean-Output-parts Number Before First Block is presented to be a new evaluating criterion in this paper to evaluate scheduling policies for Closed Re-entrant Lines(CRL). Simulations of four static scheduling policies verify the new criterion. In order to apply a Neuro-Dynamic Programming (NDP) method to scheduling of a CRL, cost-to-go value function and transition cost function are presented as new forms under the new criterion. In addition, the policy obtained in a less-number parts system by the NDP is applied in a more-number parts system directly, whose results are satisfactory.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, Y., Jin, H., Zhu, S., Li, M. (2006). Scheduling of Re-entrant Lines with Neuro-Dynamic Programming Based on a New Evaluating Criterion. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_134
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DOI: https://doi.org/10.1007/11760191_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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