<![CDATA[This study proposes a hybrid approach combining Frequent Itemset Mining (FIM) and Algorithms and Genetic Algorithm for product layout optimization with a case study at PT. MPI Pharmacy. The FIM Algorithms is employed to extract association rules from 1,000 beauty product sales transactions, while the Genetic Algorithm is utilized to perform product placement based on these rules generated, with storage space constraints. Implementation results demonstrate that this hybrid approach successfully identifies 18 key association rules (support >15, confidence >80%) and proposes an optimal layout configuration model that reduces customer travel distance by 25 compared to conventional layouts used by MPI Pharmacy. The Genetic Algorithm solves complex rule-based optimization problems for product placement, which are limited by traditional market basket analysis (MBA) approaches that rely solely on association rules. This hybrid sistem not only improves pharmacy operational efficiency at PT MPI (reducing service time by 18) but also increases cross-selling opportunities by 22. Hence, inventory operations management impproved efficiently. The research findings contribute to the field of retail space optimization by effectively integrating association rule mining and evolutionary computation. ]]>
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