Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/188652 
Year of Publication: 
2014
Citation: 
[Journal:] Journal of Industrial Engineering and Management (JIEM) [ISSN:] 2013-0953 [Volume:] 7 [Issue:] 5 [Publisher:] OmniaScience [Place:] Barcelona [Year:] 2014 [Pages:] 1222-1249
Publisher: 
OmniaScience, Barcelona
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.
Subjects: 
constraint programming
genetic algorithm
semiconductor capacity management
production planning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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