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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Wu, Muh-CherngSubject: search.filters.subject.genetic algorithm

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    Scheduling of multiple in-line steppers for semiconductor wafer fabs
    (2014-03-01)
    Chiou, Chie-Wun
    ;
    Wu, Muh-Cherng
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    工業工程與管理學系
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    Department of Industrial Engineering and Management
    A few prior studies noticed that an in-line stepper (a bottleneck machine in a semiconductor fab) may have a capacity loss while operated in a low-yield scenario. To alleviate such a capacity loss, some meta-heuristic algorithms for scheduling a single in-line stepper were proposed. Yet, in practice, there are multiple in-line steppers to be scheduled in a fab. This article aims to enhance prior algorithms so as to deal with the scheduling for multiple in-line steppers. Compared to prior studies, this research has to additionally consider how to appropriately allocate jobs to various machines. We enhance prior algorithms by developing a chromosome-decoding scheme which can yield a job-allocation decision for any given chromosome (or job sequence). Seven enhanced versions of meta-heuristic algorithms (genetic algorithm, Tabu, GA-Tabu, simulated annealing, M-MMAX, PACO and particle swarm optimisation) were then proposed and tested. Numerical experiments indicate that the GA-Tabu method outperforms the others. In addition, the lower the process yield, the better is the performance of the GA-Tabu algorithm.
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    Design of BOM configuration for reducing spare parts logistic costs
    (2008-05-04)
    Wu, Muh-Cherng
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    Hsu, Yang-Kang
    ;
    工業工程與管理學系
    ;
    Department of Industrial Engineering and Management
    This paper proposes an approach to reduce the total operational cost of a spare part logistic system by appropriately designing the BOM (bill of material) configuration. A spare part may have several vendors. Parts supplied by different vendors may vary in failure rates and prices - the higher the failure rate, the lower the price. Selecting vendors for spare parts is therefore a trade-off decision. Consider a machine where the BOM is composed of s critical parts and each part has k vendors. The number of possible BOM configurations for the machine is then k(s). For each BOM configuration, we can use OPUS10 (proprietary software) to calculate an optimum inventory policy and its associated total logistic cost. Exhaustively searching the solution space by OPUS10 can yield an optimal BOM configuration; however, it may be formidably time-consuming. To remedy the time-consuming problem, this research proposes a GA-neural network approach to solve the BOM configuration design problem. A neural network is developed to efficiently emulate the function of OPUS 10 and a GA (genetic algorithm) is developed to quickly find a near-optimal BOM configuration. Experiment results indicate that the approach can obtain an effective BOM configuration efficiently. (c) 2007 Elsevier Ltd. All rights reserved.
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    A multiple criteria decision for trading capacity between two semiconductor fabs
    (2008-10-01)
    Wu, Muh-Cherng
    ;
    Chang, Wen-Jen
    ;
    工業工程與管理學系
    ;
    Department of Industrial Engineering and Management
    This paper presents a multiple criteria decision approach for trading weekly tool capacity between two semiconductor tabs. 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. Wit 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 tabs. Compared with no trading, their method could effectively increase the two fabs' throughput for a longer period such as 8 weeks. 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 10 weeks. Experiments indicated that the multiple criteria approach indeed outperformed the previous method in terms the fabs' long-term throughput. (C) 2007 Elsevier Ltd. All rights reserved.