Computers and Industrial Engineering
An introduction to genetic algorithms
An introduction to genetic algorithms
Integrating targeted cycle-time reduction into the capital planning process
Proceedings of the 30th conference on Winter simulation
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 multiple criteria decision for trading capacity between two semiconductor fabs
Expert Systems with Applications: An International Journal
Constructing investment strategy portfolios by combination genetic algorithms
Expert Systems with Applications: An International Journal
An efficient approach to cross-fab route planning for wafer manufacturing
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
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
Capacity sharing in a network of independent factories: A cooperative game theory approach
Robotics and Computer-Integrated Manufacturing
A genetic algorithm for scheduling dual flow shops
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
This paper presents a capacity trading method for two semiconductor fabs that have established a capacity-sharing partnership. A fab that is predicted to have insufficient capacity at some workstations in a short-term period (e.g. one week) could purchase tool capacity from its partner fab. The population of such a capacity-trading portfolio may be quite huge. The proposed method involves three modules. We first use discrete-event simulation to identify the trading population. Secondly, some randomly sampled trading portfolios with their performance measured by simulation are used to develop a neural network, which can efficiently evaluate the performance of a trading portfolio. Thirdly, a genetic algorithm (GA) embedded with the developed neural network is used to find a near-optimal trading portfolio from the huge trading population. Experiment results indicate that the proposed trading method outperforms two other benchmarked methods in terms of number of completed operations, number of wafer outs, and mean cycle time.