A Constraint programming-based genetic algorithm for capacity output optimization
Abstract: 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.
Keywords: constraint
programming, genetic algorithm, semiconductor capacity management, production
planning
Author: Kate Ean Nee Goh, Jeng
Feng Chin, Wei Ping Loh, Melissa Chea-Ling Tan
Journal Code: jptindustrigg140071
