| 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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