A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A dispatching rule for photolithography scheduling with an on-line rework strategy
Computers and Industrial Engineering
A short-term capacity trading method for semiconductor fabs with partnership
Expert Systems with Applications: An International Journal
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Long-term tool elimination planning for a wafer fab
Computers and Industrial Engineering
A multiple criteria decision for trading capacity between two semiconductor fabs
Expert Systems with Applications: An International Journal
Raising the hit rate for wafer fabrication by a simple constructive heuristic
Expert Systems with Applications: An International Journal
An integrated approach to the design and operation for spare parts logistic systems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper presents a scheduling algorithm for an in-line stepper in low-yield scenarios, which mostly appear in cases when new process/production is introduced. An in-line stepper is a bottleneck machine in a semiconductor fab. Its interior comprises a sequence of chambers, while its exterior is a dock equipped with several ports. The transportation unit for entry of each port is a job (a group of wafers), while that for each chamber is a piece of wafer. This transportation incompatibility may lead to a capacity-loss, in particular in low-yield scenarios. Such a capacity-loss could be alleviated by effective scheduling. The proposed scheduling algorithm, called GA-Tabu, is a combination of a genetic algorithm (GA) and a tabu search technique. Numerical experiments indicate that the GA-Tabu algorithm outperforms seven benchmark ones. In particular, the GA-Tabu algorithm outperforms a prior GA both in solution quality and computation efforts.