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Hybrid Ant Colony Algorithm for Batch Scheduling in Semiconductor Furnace Operation
Received date: 2018-07-02
Online published: 2020-08-18
Furnace district is one of the main bottlenecks in semiconductor fabrication, which has a great influence on the entire production line. The current scheduling research in the furnace district mainly focuses on dispatching rules, and the constraints considered are relatively simple. The previous research ignores not only the multi-machine scheduling which contains front and rear procedures but also the re-entrant characteristic of the wafer fabrication. This paper focuses on the scheduling problem of β1→β2 type for minimizing the meaning flow time (MFT) in furnace district. The constraints consist of limited waiting time, incompatible families, and re-entrant flow. It builds a novel β1→β2 model about the scheduling problem, and decomposes the problem into three stages: batch forming, machine selecting, and batch sorting. An algorithm based on the hybrid ant colony optimization algorithm is proposed, which batches the jobs by using a variable threshold control strategy, and sorts these batches by a hybrid ant colony optimization algorithm. According to the results of 54 sets of different scales based on historical production data, it is concluded that the performance of the hybrid ant colony optimization (ACO) algorithm is better than several common heuristic rules and the genetic algorithm. The proposed hybrid-ACO algorithm is applied to the actual wafer production line, which can effectively reduce the water flow time in the production process.
JIANG Xiaokang, ZHANG Peng, LÜ Youlong, ZHAO Xinming, ZHANG Jie . Hybrid Ant Colony Algorithm for Batch Scheduling in Semiconductor Furnace Operation[J]. Journal of Shanghai Jiaotong University, 2020 , 54(8) : 792 -804 . DOI: 10.16183/j.cnki.jsjtu.2018.232
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