Computers and Industrial Engineering
Integrating targeted cycle-time reduction into the capital planning process
Proceedings of the 30th conference on Winter simulation
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Capacity Optimization Planning System (Caps)
Interfaces
Tool capacity planning in semiconductor manufacturing
Computers and Operations Research
Capacity planning with capability for multiple semiconductor manufacturing fabs
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
A distributed shifting bottleneck heuristic for complex job shops
Computers and Industrial Engineering
A short-term capacity trading method for semiconductor fabs with partnership
Expert Systems with Applications: An International Journal
A GA-Tabu algorithm for scheduling in-line steppers in low-yield scenarios
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
This paper presents a multiple criteria decision approach for trading weekly tool capacity between two semiconductor fabs. Due to the high-cost characteristics of tools, a semiconductor company with multiple fabs (factories) may weekly trade their tool capacities. That is, a lowly utilized workstation in one fab may sell capacity to its highly utilized counterpart in the other fab. Wu and Chang [Wu, M. C., & Chang, W. J. (2007). A short-term capacity trading method for semiconductor fabs with partnership. Expert Systems with Application, 33(2), 476-483] have proposed a method for making weekly trading decisions between two wafer fabs. Compared with no trading, their method could effectively increase the two fabs' throughput for a longer period such as 8weeks. However, their trading decision-making is based on a single criterion-number of weekly produced operations, which may still leave a space for improving. We therefore proposed a multiple criteria trading decision approach in order to further increase the two fabs' throughput. The three decision criteria are: number of operations, number of layers, and number of wafers. This research developed a method to find an optimal weighting vector for the three criteria. The method firstly used NN+GA (neural network+genetic algorithm) to find an optimal trading decision in each week, and then used DOE+RSM (design of experiment+response surface method) to find an optimal weighting vector for a longer period, say 10weeks. Experiments indicated that the multiple criteria approach indeed outperformed the previous method in terms the fabs' long-term throughput.