The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

1. 서 론
2. 이론적 배경
    2.1 유전알고리즘
    2.2 이산사건 시뮬레이션 모듈
3. 문제정의 및 유전알고리즘
    3.1 문제정의
    3.2 유전알고리즘 구조 및 흐름도
4. 시뮬레이션
    4.1 이산사건 시뮬레이션 모델링
    4.2 시뮬레이션 모델 검증
    4.3 유전알고리즘 적용결과
5. 결 론
References

• Hong-Mo Yang(Department of Smart Production and Management Engineering, Hanbat National University) | 양홍모 (국립한밭대학교 스마트생산경영공학과)
• Moonsoo Shin(Department of Industrial and Management Engineering, Hanbat National University) | 신문수 (국립한밭대학교 산업경영공학과) Corresponding author