AMHS for 300 mm wafer

Kurosaki, Ryo; Nagao, Nobunao; Komada, H.; Watanabe, Yukihiro; Yano, Hirokazu

doi:10.1109/issm.1997.664525

Cited by 22 publications

(7 citation statements)

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“…The perimeter layout had the worst performance in terms of delivery time, vehicle utilization, and track length requirement. Several authors then studied interbay and intrabay AMHS design issues (Kurosaki, Nagao, Komada, Watanabe, & Yano, 1996;Paprotny, Zhao, & Mackulak, 1999;Pillai, Quinn, Kryder, & Charlson, 1999;Ting & Tanchoco, 2001;Wu, Rayter, Paprotny, Mackulak, & Yngve, 1999). Once fab layout and material handling equipment have been determined, performance analysis can be performed to evaluate AMHS design alternatives.…”

Section: Relevant Literaturementioning

confidence: 99%

Multiple response optimization in a fully automated FAB: an integrated tool and vehicle dispatching strategy∗

Tyan

Chen

et al. 2004

Computers & Industrial Engineering

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Section: Relevant Literaturementioning

confidence: 99%

Multiple response optimization in a fully automated FAB: an integrated tool and vehicle dispatching strategy∗

Tyan

Chen

et al. 2004

Computers & Industrial Engineering

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“…Some investigations were also made on the dispatching rules where the goal is to improve the performance of AMHS, for example, Kurosaki et al (1997), Mackulak and Savory (2001), and Wang and Lin (2004). In particular, Mackulak and Savory (2001) utilized simulation and a series of experiments to compare the performance of two layouts for the intrabay system.…”

Section: Introductionmentioning

confidence: 99%

A simulation-based framework for multi-objective vehicle fleet sizing of automated material handling systems: an empirical study

Chang

Kuo

2014

Journal of Simulation

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Automated materials handling systems (AMHS) play a key role in semiconductor manufacturing. Vehicle fleet sizing is one of the critical issues when designing an effective AMHS. However, due to complexity of AMHS design and uncertainty involved in the production process, for example, random processing time, vehicle fleet sizing is a challenging problem, especially when there are multiple objectives, for example, minimized delivery time and maximized delivered lots are simultaneously desired. In this paper, we formulate the multi-objective vehicle fleet sizing problem and propose a framework that integrates simulation optimization and data envelopment analysis techniques to determine the optimal vehicle fleet size under multiple objectives for the AMHS. Numerical experiments show that the proposed framework allows for better performance of AMHS than the traditional methods. Moreover, an empirical study conducted at the end verifies the effectiveness and the viability of the proposed framework in real settings.

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“…Some previous research have been devoted to vehicle management of AMHS in which the attention was put on the AMHS configuration to avoid vehicle collision, deadlock or blockage, and to prevent the paralysis of handling system [1,2,3,4]. Some investigations were also made on the dispatching rules for the vehicles to improve the performance of the system [5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic vehicle allocation in Automated Material Handling System

Lin

Chang

Huang

2010

2010 IEEE 17Th International Conference on Industrial Engineering and Engineering Management

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The Automated Materials Handling System (AMHS) in semiconductor industry is a significant to reduce the wafer cycle times and enhance the fab productivity. The determination of vehicle number, however, is challenging due to the complicated facility layout and the multiple reentries required in the manufacturing process. Previous research used to solve this problem based on the mathematical programming model, which does not take into account the highly variable and uncertain events in the process. In this research, we build a simulation model to account for the uncertainties existing in the manufacturing process. Furthermore, we propose a simulation optimization approach to solve the problem, i.e., to determine the optimal number of vehicles of the AMHS. An empirical study is conducted at the end to show the viability of the proposed method.

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AMHS for 300 mm wafer

Cited by 22 publications

References 0 publications

Multiple response optimization in a fully automated FAB: an integrated tool and vehicle dispatching strategy∗

Multiple response optimization in a fully automated FAB: an integrated tool and vehicle dispatching strategy∗

A simulation-based framework for multi-objective vehicle fleet sizing of automated material handling systems: an empirical study

Dynamic vehicle allocation in Automated Material Handling System

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