Determining Optimal WIP Level and Buffer Size Using Simulated Annealing in Semiconductor Production Line

반도체 생산라인에서 SA를 이용한 최적 WIP수준과 버퍼사이즈 결정

  • Jeong, Jaehwan (Graduate School of Consulting Kumoh National Institute of Technology) ;
  • Jang, Sein (School of Industrial Engineering Kumoh National Institute of Technology) ;
  • Lee, Jonghwan (School of Industrial Engineering Kumoh National Institute of Technology)
  • 정재환 (금오공과대학교 컨설팅대학원) ;
  • 장세인 (금오공과대학교 산업공학과) ;
  • 이종환 (금오공과대학교 산업공학과)
  • Received : 2021.08.26
  • Accepted : 2021.09.13
  • Published : 2021.09.30

Abstract

The domestic semiconductor industry can produce various products that will satisfy customer needs by diversifying assembly parts and increasing compatibility between them. It is necessary to improve the production line as a method to reduce the work-in-process inventory (WIP) in the assembly line, the idle time of the worker, and the idle time of the process. The improvement of the production line is to balance the capabilities of each process as a whole, and to determine the timing of product input or the order of the work process so that the time required between each process is balanced. The purpose of this study is to find the optimal WIP and buffer size through SA (Simulated Annealing) that minimizes lead time while matching the number of two parts in a parallel assembly line with bottleneck process. The WIP level and buffer size obtained by the SA algorithm were applied to the CONWIP and DBR systems, which are the existing production systems, and the simulation was performed by applying them to the new hybrid production system. Here, the Hybrid method is a combination of CONWIP and DBR methods, and it is a production system created by setting new rules. As a result of the Simulation, the result values were derived based on three criteria: lead time, production volume, and work-in-process inventory. Finally, the effect of the hybrid production method was verified through comparative analysis of the result values.

Keywords

Acknowledgement

This paper was supported by Kumoh National Institute of Technology.

References

  1. Wallace J.Hopp and Mark L.Spearman, The Laws of Manufacturing Science, HanKyung Publishing Company, pp.1-791, 2005
  2. I Ryoo, J Kim, and J. Lee "Production Control in Multiple Bottleneck Processes using Genetic Algorithm", Journal of Society of Korea Industrial Systems Engineering, v. 41, n.1, pp. 102-109, 2018 https://doi.org/10.11627/jkise.2018.41.1.102
  3. Won Geun Kim, "Comparative analysis of performance values of CONWIP System and Kanban System", Kyung Hee University, 2007
  4. J Kim, Ji Yong Jeong, J. Lee "Optimization Work-In Process Parameter using Genetic Algorithm", Journal of Society of Korea Industrial Systems Engineering, v. 40, n.2, pp. 79-86, 2017 https://doi.org/10.11627/jkise.2017.40.1.079
  5. Woo Sang Kim, "Production Management System Construction based on TOC", Chonnam National University, 2009
  6. Seung Nam Kim, Min Sun Hong, Suk Chul Rim, "Development of the CTP reflecting DBR Buffer", Journal of the Korean Operations Research and Management Science Society, pp.396-399, 2004.
  7. Sun Young Lee, "Multi-objective optimization using simulated annealing and multi-objective metrics", Korea Advanced Institute of Science and Technology, 2006
  8. Byeong-Gil Lee, Minseok Byun, Yeojin Kim and Jonghwan Lee, "Determination of Optimal Buffer Size for Semiconductor Production System using Harmony Search Algorithm", Journal of the Semiconductor & Display Technology, v.19, n.4, pp.39-45, 2021