Modelling and Rescheduling of a Re-entrant Wafer Fabrication Line Involving Machine Unreliability
A methodology for rescheduling a semiconductor fabrication line in the presence of predicted and unpredicted machine unreliability and job demand is presented. The proposed methodology consists of four major steps. (1) Representation of the fabrication line: the fabrication line is represented using a Neuro-Expert Petri net (NEPN) model. The entire fabrication line is decomposed into a 'two machine and one buffer' subsystem and represented using the NEPN model. (2) Estimation of failure and repair rates: the probabilistic machine failure and repair rates are determined using the method proposed by Jeong and Kim (1998). (3) Activation of the rescheduling algorithm: the rescheduling algorithm is activated after the planned mean time to reschedule the fabrication line ( TPRS4431math001). TPRS4431math002 is calculated by taking into account the estimated machine failure and repair rates, and the threshold queue limits of the waiting jobs. The rescheduling algorithm is also activated in cases of unpredictable machine breakdown or repair, and where the job queue length exceeds a certain threshold. (4) Execution of the rescheduling algorithm: the rescheduling algorithm determines whether or not to reschedule a job onto another machine by taking into account factors such as due dates and profits generated by the jobs. The proposed solution methodology is explained using an illustrative example. The computational results developed for the makespan criteria reveal that the proposed methodology is reliable and performs relatively well.
R. Kumar et al., "Modelling and Rescheduling of a Re-entrant Wafer Fabrication Line Involving Machine Unreliability," International Journal of Production Research, Taylor & Francis, Nov 2004.
The definitive version is available at http://dx.doi.org/10.1080/00207540410001716552
Engineering Management and Systems Engineering
Keywords and Phrases
Logistics; Manufacturing Engineering; Manufacturing Industries; Manufacturing Technology; Operations Management; Production & Quality Control Management; Production Research & Economics; Production Systems; Production Systems & Automation
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