Aggregate production planning with small data in TFT-LCD manufacturing

Chia Yen Lee; Ming Chien Chiang

doi:10.1109/CoASE.2015.7294151

Aggregate production planning with small data in TFT-LCD manufacturing

Chia Yen Lee
, Ming Chien Chiang

Institute of Manufacturing Information and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This study proposes a two-phase research framework to address the problem of capacity-demand mismatch in the high-tech industry. In the first stage, due to the characteristics of small data, we apply the virtual data generation process (VDGP) to support the data learning for demand forecast. In the second stage, based on the demand scenarios, a robust capacity decision is provided by the stochastic programming (SP) technique and the minimax regret (MMR) technique addressing demand uncertainty. We conduct an empirical study of a TFT-LCD firm to validate the proposed framework. That result shows that the proposed framework, in particular the SP technique, provides a robust capacity levels addressing the problem of capacity-demand mismatch.

Original language	English
Title of host publication	2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publication	Automation for a Sustainable Future, CASE 2015
Publisher	IEEE Computer Society
Pages	647-648
Number of pages	2
ISBN (Electronic)	9781467381833
DOIs	https://doi.org/10.1109/CoASE.2015.7294151
Publication status	Published - 2015 Oct 7
Event	11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden Duration: 2015 Aug 24 → 2015 Aug 28

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2015-October
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Other

Other	11th IEEE International Conference on Automation Science and Engineering, CASE 2015
Country/Territory	Sweden
City	Gothenburg
Period	15-08-24 → 15-08-28

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/CoASE.2015.7294151

Cite this

Lee, C. Y., & Chiang, M. C. (2015). Aggregate production planning with small data in TFT-LCD manufacturing. In 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015 (pp. 647-648). Article 7294151 (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October). IEEE Computer Society. https://doi.org/10.1109/CoASE.2015.7294151

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title = "Aggregate production planning with small data in TFT-LCD manufacturing",

abstract = "This study proposes a two-phase research framework to address the problem of capacity-demand mismatch in the high-tech industry. In the first stage, due to the characteristics of small data, we apply the virtual data generation process (VDGP) to support the data learning for demand forecast. In the second stage, based on the demand scenarios, a robust capacity decision is provided by the stochastic programming (SP) technique and the minimax regret (MMR) technique addressing demand uncertainty. We conduct an empirical study of a TFT-LCD firm to validate the proposed framework. That result shows that the proposed framework, in particular the SP technique, provides a robust capacity levels addressing the problem of capacity-demand mismatch.",

author = "Lee, \{Chia Yen\} and Chiang, \{Ming Chien\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th IEEE International Conference on Automation Science and Engineering, CASE 2015 ; Conference date: 24-08-2015 Through 28-08-2015",

year = "2015",

month = oct,

day = "7",

doi = "10.1109/CoASE.2015.7294151",

language = "English",

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Lee, CY & Chiang, MC 2015, Aggregate production planning with small data in TFT-LCD manufacturing. in 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015., 7294151, IEEE International Conference on Automation Science and Engineering, vol. 2015-October, IEEE Computer Society, pp. 647-648, 11th IEEE International Conference on Automation Science and Engineering, CASE 2015, Gothenburg, Sweden, 15-08-24. https://doi.org/10.1109/CoASE.2015.7294151

Aggregate production planning with small data in TFT-LCD manufacturing. / Lee, Chia Yen; Chiang, Ming Chien.
2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society, 2015. p. 647-648 7294151 (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Aggregate production planning with small data in TFT-LCD manufacturing

AU - Lee, Chia Yen

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N2 - This study proposes a two-phase research framework to address the problem of capacity-demand mismatch in the high-tech industry. In the first stage, due to the characteristics of small data, we apply the virtual data generation process (VDGP) to support the data learning for demand forecast. In the second stage, based on the demand scenarios, a robust capacity decision is provided by the stochastic programming (SP) technique and the minimax regret (MMR) technique addressing demand uncertainty. We conduct an empirical study of a TFT-LCD firm to validate the proposed framework. That result shows that the proposed framework, in particular the SP technique, provides a robust capacity levels addressing the problem of capacity-demand mismatch.

AB - This study proposes a two-phase research framework to address the problem of capacity-demand mismatch in the high-tech industry. In the first stage, due to the characteristics of small data, we apply the virtual data generation process (VDGP) to support the data learning for demand forecast. In the second stage, based on the demand scenarios, a robust capacity decision is provided by the stochastic programming (SP) technique and the minimax regret (MMR) technique addressing demand uncertainty. We conduct an empirical study of a TFT-LCD firm to validate the proposed framework. That result shows that the proposed framework, in particular the SP technique, provides a robust capacity levels addressing the problem of capacity-demand mismatch.

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M3 - Conference contribution

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ER -

Aggregate production planning with small data in TFT-LCD manufacturing

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