A Constraint Programming-based Genetic Algorithm (CPGA) for Capacity Output Optimization
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 v...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
OmniaScience
2014
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| Subjects: | |
| Online Access: | http://eprints.usm.my/37999/ http://eprints.usm.my/37999/1/A_Constraint_programming-based_genetic_algorithm_for_capacity_output_optimization.pdf |
| Summary: | 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. |
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