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• W1982193504 abstract "Uncertainty in semiconductor fabrication facilities (fabs) requires scheduling methods to attain quick real-time responses. They should be well tuned to track the changes of a production environment to obtain better operational performance. This paper presents an adaptive dispatching rule (ADR) whose parameters are determined dynamically by real-time information relevant to scheduling. First, we introduce the workflow of ADR that considers both batch and non-batch processing machines to obtain improved fab-wide performance. It makes use of such information as due date of a job, workload of a machine, and occupation time of a job on a machine. Then, we use a backward propagation neural network (BPNN) and a particle swarm optimization (PSO) algorithm to find the relations between weighting parameters and real-time state information to adapt these parameters dynamically to the environment. Finally, a real fab simulation model is used to demonstrate the proposed method. The simulation results show that ADR with constant weighting parameters outperforms the conventional dispatching rule on average; ADR with changing parameters tracking real-time production information over time is more robust than ADR with constant ones; and further improvements can be obtained by optimizing the weights and threshold values of BPNN with a PSO algorithm." @default.
• W1982193504 created "2016-06-24" @default.
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• W1982193504 date "2013-04-01" @default.
• W1982193504 modified "2023-10-10" @default.
• W1982193504 title "Adaptive Dispatching Rule for Semiconductor Wafer Fabrication Facility" @default.
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• W1982193504 doi "https://doi.org/10.1109/tase.2012.2221087" @default.
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