Research Article
A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory
Table 1
The differences between the proposed methodology and the previous methods.
| Rule | Number of objectives | Objectives | Number of adjustable parameters | Optimized? | How to derive the rule? |
| NFSMCT | 1 | Average cycle time | 1 | No | (i) Generalizing FSMCT | 1f-TNFSVCT | 1 | Cycle time standard variation | 1 | No | (i) Generalizing FSVCT (ii) Adding adjustable parameters | 1f-TNFSMCT | 1 | Average cycle time | 1 | No | (i) Generalizing FSMCT (ii) Adding adjustable parameters | 2f-TNFSVCT | 1 | Cycle time standard deviation | 2 | No | (i) Generalizing FSVCT (ii) Adding adjustable parameters | 4f-biNFS | 2 | Average cycle time, cycle time standard deviation | 2 | Yes | (i) Fusing FSVCT and FSMCT (ii) Adding adjustable parameters | The proposed methodology | 2 | Average cycle time, cycle time standard deviation | 2 | Yes | (i) Fusing 2f-TFSMCT and 2f-TNFSVCT (ii) Nonlinear programming |
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