A constraint programming-based genetic algorithm (CPGA) for capacity output optimization

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Document typeArticle
Defense date2014-12
PublisherOmniaScience
Rights accessOpen Access
Abstract
Purpose: The manuscript presents an investigation into a constraint programming-based
genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing
company.
Design/methodology/approach: In the first stage, constraint programming defining the
relationships between variables was formulated into the objective function. A genetic algorithm
model was created in the second stage to optimize capacity output. Three demand scenarios
were applied to test the robustness of the proposed algorithm.
Findings: CPGA improved both the machine utilization and capacity output once the
minimum requirements of a demand scenario were fulfilled. Capacity outputs of the three
scenarios were improved by 157%, 7%, and 69%, respectively.
Research limitations/implications: The work relates to aggregate planning of machine
capacity in a single case study. The constraints and constructed scenarios were therefore
industry-specific.Practical implications: Capacity planning in a semiconductor manufacturing facility need to
consider multiple mutually influenced constraints in resource availability, process flow and
product demand. The findings prove that CPGA is a practical and an efficient alternative to
optimize the capacity output and to allow the company to review its capacity with quick
feedback.
Originality/value: The work integrates two contemporary computational methods for a real
industry application conventionally reliant on human judgement.
CitationGoh, Kate Ean Nee; Chin, Jeng Feng; Loh, Wei Ping. A constraint programming-based genetic algorithm (CPGA) for capacity output optimization. "Journal of Industrial Engineering and Management", Desembre 2014, vol. 7, núm. 5, p. 1222-1249.
DLB-28744-2008
ISSN2013-0953
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