Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
Comparison of dispatching rules for semiconductor manufacturing using large facility models
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
From differential equations to PDC controller design via numerical transformation
Computers in Industry
Computers and Industrial Engineering
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Semiconductor wafer fabrication system (SWFS) is one of the most complicate discrete processing systems in the world. As the wafer size grows from 200 to 300mm and then to 450mm in recent years, the interbay automated material handling system (AMHS) has been widely adopted. How to improve the overall efficiency of AMHS has therefore become a crucial and urgent problem to wafer manufacturers. However, the large-scale, dynamic and stochastic production environment significantly substantiates the complexity of the scheduling problem for interbay AMHS. Aiming to meet the demand of multiple-objective optimization, composite dispatching rules (CDR) are applied. The system parameters, including wafer cassettes due date, waiting time, and stocker buffer status are simultaneously considered. In order that the composite dispatching rules can be used in real-life dynamic production, a genetic programming based CDR generator is proposed. Discrete event simulation models are constructed using the eM-Plant software to simulate the 300mm SWFS. The numerical study indicates that by using the generated composite dispatching rules the transport efficiency is improved, meanwhile, the wafer throughput is increased and the processing cycle time is shortened. The experimental results also demonstrate that the GP-based generating algorithm is effective and efficient for a dynamic environment. Further comparisons with other scheduling methods show that the proposed approach performs better in most scenarios.