Aggregate production planning with small data in TFT-LCD manufacturing

Chia Yen Lee, Ming Chien Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publicationAutomation for a Sustainable Future, CASE 2015
PublisherIEEE Computer Society
Pages647-648
Number of pages2
ISBN (Electronic)9781467381833
DOIs
Publication statusPublished - 2015 Oct 7
Event11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden
Duration: 2015 Aug 242015 Aug 28

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2015-October
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other11th IEEE International Conference on Automation Science and Engineering, CASE 2015
CountrySweden
CityGothenburg
Period15-08-2415-08-28

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • 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). [7294151] (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October). IEEE Computer Society. https://doi.org/10.1109/CoASE.2015.7294151