期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 1, 页码 282-287出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.05.001
关键词
Dynamic job shop; Multi-objective scheduling; Variable neighborhood search; Artificial neural networks
Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions. (C) 2009 Published by Elsevier Ltd.
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