This paper seeks to present a multi-control method that can contribute to effective control of the production line with multiple bottleneck processes. The multi-control method is the production system that complements shortcomings of CONWIP and DBR, and it is designed to determine the raw material input according to the WIP level of two bottleneck processes and WIP level of total process. The effectiveness of the production system developed by applying the multi-control method was verified by the following three procedures. Raw material input conditions of the multi-control method are as follows. First, raw materials are go into the production line when the number of the total process WIP is lower than established number of WIP in total process and first process is idle. Second, raw materials are introduced when the number of WIP of two bottleneck processes is lower than the established number of WIP of each bottleneck process. Third, raw materials are introduced when the first process and in front of bottleneck process are idle even if the number of WIP in the total process is less than established number of WIP of the total process. The production line with two bottleneck processes was selected as the condition for production environment, and the production process modeling of CONWIP, DBR and multi-control production method was defined according to the production condition. And the optimum limited WIP level suitable for each system was obtained by applying a genetic algorithm to determine the total limited number of WIP of CONWIP, the limited number of WIP of DBR bottleneck process, the number of WIP in the total process of multi-control method and the limited number of WIP of bottleneck process. The limited number of WIP of CONWIP, DBR and multi-control method obtained by the genetic algorithm were applied to ARENA modeling, which is simulation software, and a simulation was conducted to derive result values on the basis of three criteria such as production volume, lead time and number of goods in-progress.

1. 서 론
 2. 기존 논문 연구
 3. 실험 조건 및 프로세스 정의
  3.1 실험개요
  3.2 프로세스 정의
  3.3 Genetic Algorithm(GA) 적용
 4. 결 과
  4.1 시뮬레이션 결과
  4.2 통계적 검정
 5. 결 론
 References

• 류일환(금오공과대학교 경영학과) | Ilhwan Ryoo (Department of Business Administration, Kumoh National Institute of Technology)
• 이정호(금오공과대학교 산업공학과) | Jung-ho Lee (The School of Industrial Engineering, Kumoh National Institute of Technology)
• 이종환(금오공과대학교 산업공학과) | Jonghwan Lee (The School of Industrial Engineering, Kumoh National Institute of Technology) Corresponding Author