A Constraint programming-based genetic algorithm for capacity output optimization

• Goh, Kate Ean Nee ^[1] ; Chin, Jeng Feng ^[1] ; Loh, Wei Ping ^[1] ; Tan, Melissa Chea-Ling ^[2]
  1. [1] Universiti Sains Malaysia

     Universiti Sains Malaysia

     Malasia

     
  2. [2] Ines-Nathan Creative Research Center (IN-CRC). 11, Lorong Desa Permai 2, Taman Desa Permai, 14300 Nibong Tebal.
• Localización: Journal of Industrial Engineering and Management, ISSN-e 2013-0953, Vol. 7, Nº. 5, 2014, págs. 1222-1249
• Idioma: inglés
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• Resumen

  • 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.