Deterministic model for customized pilot manufacture production with various backplane sizes

Yao Huei Huang, Hao Chun Lu, I. Fan Liu

Research output: Contribution to journalArticle

Abstract

More and more backplane manufacturing industries need a customized pilot production scheme that can produce various sizes of backplanes to support the innovation service of the user-oriented design. The deterministic model of the customized pilot production scheme, which utilizes binary variables and branch-and-bound scheme, can obtain the global optimization; however, this model is hard to solve due to the numerous binary variables and the unbalanced branch-and-bound tree. This study adopted a novel incremental encoding formulation to conquer the unbalanced branch-and-bound issue and a range reduction technique to reduce the required number of binary variables in the proposed model. The proposed model has a faster computation speed compared with the original deterministic model. Numerical experiments further confirmed that the proposed model could obtain a global optimization within a reasonable time for all converged instances of the backplane manufacturing industry and benchmark datasets.

Original languageEnglish
Pages (from-to)491-504
Number of pages14
JournalApplied Mathematical Modelling
Volume71
DOIs
Publication statusPublished - Jul 1 2019

Fingerprint

Deterministic Model
Binary Variables
Branch-and-bound
Manufacturing Industries
Global Optimization
Global optimization
Model
Encoding
Numerical Experiment
Benchmark
Formulation
Industry
Innovation
Range of data
Experiments

Keywords

  • Deterministic method
  • Incremental encoding formulation
  • Pilot production
  • Range reduction technique
  • Two-dimensional cutting stock problem

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics

Cite this

Deterministic model for customized pilot manufacture production with various backplane sizes. / Huang, Yao Huei; Lu, Hao Chun; Liu, I. Fan.

In: Applied Mathematical Modelling, Vol. 71, 01.07.2019, p. 491-504.

Research output: Contribution to journalArticle

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