Solving the mask data preparation scheduling problem using meta-heuristics

Kuo Ching Ying; Shih Wei Lin; Chien Yi Huang; Memphis Liu; Chia Tien Lin

doi:10.1109/ACCESS.2019.2899601

Solving the mask data preparation scheduling problem using meta-heuristics

Kuo Ching Ying, Shih Wei Lin^*, Chien Yi Huang, Memphis Liu, Chia Tien Lin

^*Corresponding author for this work

Research output: Contribution to journal › Journal Article › peer-review

1 Scopus citations

Abstract

Mask data preparation (MDP) is a part of the mask data process for fabricating semiconductors, and its importance has commonly been neglected. This paper proposes an integer linear programming model and two meta-heuristics, a genetic algorithm (GA) and simulated annealing (SA), for solving the MDP scheduling problem (MDPSP). The proposed meta-heuristics are empirically evaluated using 768 simulation instances of MDPSP based on the characteristics of a real technology company and compared with the most commonly used first-come, first-served method. The experimental results reveal that the proposed GA and SA algorithms can critically improve the manufacturing schedule for semiconductor factories.

Original language	English
Article number	8642834
Pages (from-to)	24192-24203
Number of pages	12
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2019.2899601
State	Published - 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

Scheduling
genetic algorithm
integer linear programming
mask data preparation
meta-heuristics
simulated annealing

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10.1109/ACCESS.2019.2899601

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title = "Solving the mask data preparation scheduling problem using meta-heuristics",

abstract = "Mask data preparation (MDP) is a part of the mask data process for fabricating semiconductors, and its importance has commonly been neglected. This paper proposes an integer linear programming model and two meta-heuristics, a genetic algorithm (GA) and simulated annealing (SA), for solving the MDP scheduling problem (MDPSP). The proposed meta-heuristics are empirically evaluated using 768 simulation instances of MDPSP based on the characteristics of a real technology company and compared with the most commonly used first-come, first-served method. The experimental results reveal that the proposed GA and SA algorithms can critically improve the manufacturing schedule for semiconductor factories.",

keywords = "Scheduling, genetic algorithm, integer linear programming, mask data preparation, meta-heuristics, simulated annealing",

author = "Ying, {Kuo Ching} and Lin, {Shih Wei} and Huang, {Chien Yi} and Memphis Liu and Lin, {Chia Tien}",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2019",

doi = "10.1109/ACCESS.2019.2899601",

language = "英语",

volume = "7",

pages = "24192--24203",

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T1 - Solving the mask data preparation scheduling problem using meta-heuristics

AU - Ying, Kuo Ching

AU - Lin, Shih Wei

AU - Huang, Chien Yi

AU - Liu, Memphis

AU - Lin, Chia Tien

PY - 2019

Y1 - 2019

N2 - Mask data preparation (MDP) is a part of the mask data process for fabricating semiconductors, and its importance has commonly been neglected. This paper proposes an integer linear programming model and two meta-heuristics, a genetic algorithm (GA) and simulated annealing (SA), for solving the MDP scheduling problem (MDPSP). The proposed meta-heuristics are empirically evaluated using 768 simulation instances of MDPSP based on the characteristics of a real technology company and compared with the most commonly used first-come, first-served method. The experimental results reveal that the proposed GA and SA algorithms can critically improve the manufacturing schedule for semiconductor factories.

AB - Mask data preparation (MDP) is a part of the mask data process for fabricating semiconductors, and its importance has commonly been neglected. This paper proposes an integer linear programming model and two meta-heuristics, a genetic algorithm (GA) and simulated annealing (SA), for solving the MDP scheduling problem (MDPSP). The proposed meta-heuristics are empirically evaluated using 768 simulation instances of MDPSP based on the characteristics of a real technology company and compared with the most commonly used first-come, first-served method. The experimental results reveal that the proposed GA and SA algorithms can critically improve the manufacturing schedule for semiconductor factories.

KW - Scheduling

KW - genetic algorithm

KW - integer linear programming

KW - mask data preparation

KW - meta-heuristics

KW - simulated annealing

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DO - 10.1109/ACCESS.2019.2899601

M3 - 文章

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SN - 2169-3536

VL - 7

SP - 24192

EP - 24203

JO - IEEE Access

JF - IEEE Access

M1 - 8642834

ER -

Solving the mask data preparation scheduling problem using meta-heuristics

Abstract

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Keywords

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