Lee, Jehun

• Operation optimization for smart factories

• Autonomous decision systems

• Scheduling for general systems

• Reinforcement and imitation learning for scheduling

• Ph.D. in Industrial & Systems Engineering, KAIST, Korea, 2021.03.~

• M.S. in Industrial Engineering, SKKU, Korea, 2016.09.~2021.02.

• B.S. in Systems Management Engineering, SKKU, Korea, 2013.03.~2016.08.

• Sejong Science High School, Korea, 2011.03.~2013.02.

2024

• J.-H. Lee and H.-J. Kim, "Graph-based imitation learning for real-time job shop dispatcher". Under review.

2023

• J.-H. Lee and H.-J. Kim, "Active schedule-based imitation learning for job shop scheduling", the Spring Conference of Korean Institute of Industrial Engineers, Jeju, Korea, 2023.

2022

• J.-H. Lee and H.-J. Kim*, "Imitation learning for real-time job shop scheduling using graph-based representation," Winter Simulation Conference, Singapore, 2022, pp. 3285-3296. [Paper]

• J.-H. Lee and H.-J. Kim, "Job shop scheduling using graph-based imitation learning", INFORMS Annual Meeting, Indiana, USA, 2022. [Poster]

• H. Yang, J.-H. Lee, S. H. Lee, S. G. Lee, H. R. Kim, and H.-J. Kim*, "A multi-manned assembly line worker assignment and balancing problem with positional constraints," IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7786-7793, 2022. [Paper]

• J.-H. Lee, D.-Y. Kim, S.-H. Cho, and H.-J. Kim, "Reinforcement learning for resource leveling in multiple projects", the Spring Conference of Korean Institute of Industrial Engineers, Jeju, Korea, 2022.

• J.-H. Lee and H.-J. Kim, "Dynamic job shop scheduling using graph-based imitation learning", the Spring Conference of Korean Institute of Industrial Engineers, Jeju, Korea, 2022.

2021

• J.-H. Lee, H.-J. Kim*, Y. Kim, Y. B. Kim, B.-H. Kim, and G.-H. Chung, "Machine learning-based periodic setup changes for semiconductor manufacturing machines," Winter Simulation Conference, Online, 2021, pp.1-10. [Paper] 

• D.-Y. Kim, J.-H.Lee, S.-H. Min, S.-H. Cho, J.-H. Park, H.-S. Min, G.-W. Kim, M.-S. Kim, and H.-J.Kim, "Resource leveling in shipyard cargo hold process through reinforcement learning", the Autumn Conference of Korean Institute of Industrial Engineers, Seoul, Korea, 2021.

• H. Yang, J.-H. Lee, and H.-J. Kim*, "Assembly line worker assignment and balancing problem with positional constraints," Advances in Production Management Systems (APMS), Online, 2021, pp. 3-11. [Paper]

• H. Yang, J.-H. Lee, and H.-J. Kim, "Operation and optimization of the automotive parts assembly line considering worker skill levels," the Summer Conference of Korea CDE, Online, 2021.

2020

• J.-H. Lee, Y. Kim, Y. B. Kim, B.-H. Kim, G.-H. Jung, and H.-J. Kim*, "A sequential search method of dispatching rules for scheduling of LCD manufacturing systems," IEEE Transactions on Semiconductor Manufacturing, vol. 33, no. 4, pp. 496-503, 2020. [Paper]

• J.-H. Lee, Y. Kim, Y. B. Kim, B.-H. Kim, G.-H. Chung, and H.-J. Kim*, "A simulation-based sequential search method for multi-objective scheduling problems of manufacturing systems," Winter Simulation Conference, Online, 2020, pp. 1943-1953. [Paper]

• H. Yang, J.-H. Lee, and H.-J. Kim, "Workforce assignment for automotive parts assembly lines," the Winter Conference of Korea CDE, Online, 2020.

• K. T. Park, J. Lee, H. J. Kim, and S. D. Noh*, "Digital twin-based cyber physical production system architectural framework for personalized production," The International Journal of Advanced Manufacturing Technology, vol. 106, no. 5, 2020, pp. 1787-1810. [Paper]

• H. Yang, J.-H. Lee, and H.-J. Kim*, "Workforce assignment with a different skill level for automotive parts assembly lines," Advances in Production Management Systems (APMS), Novi Sad, Serbia, 2020, pp. 206-212. [Paper]

2019

• J.-H. Lee, Y. Kim, Y.-B. Kim, H.-J. Kim*, B.-H. Kim, and G.-H. Chung, "A sequential search framework for selecting weights of dispatching rules in manufacturing systems," Winter Simulation Conference, Maryland, USA, 2019, pp. 2201-2211. [Paper]

• J.-H. Lee and H.-J. Kim, "A genetic algorithm for hybrid flow shop scheduling with multiple assembly stages," the Autumn Conference of Korean Institute of Industrial Engineers, Seoul, 2019.

2018

• J. Kim, J.-H. Lee, H.-J. Kim, and B. D. Chung*, "Vulnerability analysis of evacuation transportation networks," International Journal of Industrial Engineering-Theory Applications and Practice, vol. 25, no. 5, 2018, pp. 663-673. [Paper]

• J.-H. Lee, Y. Kim, J. Kim, Y.-B. Kim, H.-J. Kim*, B.-H. Kim, and G.-H. Chung, "A framework for performance analysis of dispatching rules in manufacturing systems," Winter Simulation Conference, Gothenburg, Sweden, 2018, pp. 3550-3560. [Paper]

• J.-H. Lee, S.-S. Park, and H.-J. Kim, "Rescheduling algorithms for 3D printer-based manufacturing systems," the Summer Conference of Korea CDE, Jeju, 2018.

• S.-S. Park, J.H. Lee, and H.-J. Kim, "Scheduling algorithms for 3D printer-based manufacturing systems," the Spring Conference of Korean Institute of Industrial Engineers, Gyeongju, Korea, 2018.

2017

• J. Kim, J.-H. Lee, S.-L. Choi, H.-J. Jung, Y.-B. Kim, H.-J*. Kim, B.-H. Kim, and G.-H. Chung, "Rescheduling of flexible flow shop with sequence-dependent setup times and job splitting," Winter Simulation Conference, Las Vegas, USA, 2017, pp. 3745-3755. [Paper]

• S.-S. Park, S.Y. Kim, J.H. Lee, and H.-J. Kim, "3D printer based assembly process scheduling algorithm development," the Winter Conference of Korea CDE, Pyeongchang, Korea, 2017, pp. 515-523.

1. Reinforcement learning for unrelated parallel machine scheduling problems with sequence-dependent setup times and machine eligibility

강화학습 기반 unrelated parallel machine 스케줄링 알고리즘 개발 

1. • 셋업 및 설비 적합성 제약이 존재하는 병렬 설비 스케줄링을 위한 GNN 기반의 강화학습 알고리즘 개발 

   • by Python

   • with KAIST, VMS Solutions

   • 2023.07.~2024.06.

2. Production planning with AI

생산계획 최적화를 위한 AI 기반 방법론 개발 

2. • 냉장고 생산 라인을 위한 생산계획 최적 수립 알고리즘 개발 

   • by Python

   • with KAIST, LG Electronics

   • 2023.07.~2024.02.

3. Graph-based reinforcement learning algorithm for real-time job shop scheduling

실시간 job shop 스케줄링을 위한 그래프 기반 강화학습 알고리즘  

3. • 강화학습을 활용한 실시간 디스패칭 agent 개발 

   • by Python (torch)

   • with KAIST (NRF of Korea)

   • 2022.05.~2023.02.

4. Reinforcement learning for job shop scheduling

강화학습 기반 job shop 스케줄링 알고리즘 개발 

4. • 강화학습을 활용한 실시간 디스패칭 agent 개발 

   • by Python (torch)

   • with KAIST, VMS Solutions

   • 2022.04.~2023.03.

5. Development of a wiring optimization algorithm for X-DEC slim layout

X-DEC slim layout 배선 최적화 알고리즘 개발

5. • 기판의 전극 위치가 주어진 상황에서 최적의 배선 도출 알고리즘 개발

   • by Python

   • with KAIST, SK Hynix

   • 2022.03.~2022.03. (entire 2022.03.~2022.09.)

6. Reinforcement learning-based meta-scheduling for manufacturing systems

제조시스템 최적화를 위한 강화학습 기반의 메타 스케줄링 방법론 개발 

6. • 다양한 스케줄링 문제에 적용할 수 있는 범용적인 agent 구축을 위한 meta-RL 적용  

   • by Python (torch)

   • with KAIST (NRF of Korea)

   • 2022.03.~ (entire 2022.03.~2025.02.)

7. Reinforcement learning for project scheduling

강화학습 기반 프로젝트 스케줄링 

7. • 기존 작업 스케줄이 주어진 경우 작업자 부하의 분산을 최소화하기 위한 강화학습 기반 리스케줄링 알고리즘 개발

   • by Python (torch)

   • with KAIST, Samsung Heavy Industries 

   • 2022.03.~ 2023.07. (entire 2022.03.~2024.02.)

8. Development of a reinforcement learning algorithm for workload balancing of ship cargo production

강화학습을 통한 화물창 부하 최적화

8. • 기존 작업 스케줄이 주어진 경우 작업자 부하의 분산을 최소화하기 위한 강화학습 기반 리스케줄링 알고리즘 개발 

   • by Python (torch)

   • with KAIST, Samsung Heavy Industries 

   • 2021.07.~2021.11.

9. Optimal machine assignment with machine learning algorithms

머신러닝 기반 최적 설비 대수 할당 모델 구축

9. • 반도체 FAB 생산라인의 생산성 최대화 및 셋업 시간 최소화를 위한 머신러닝 기반 주기적 할당 알고리즘 개발 

   • by C# (MozArt), Python

   • with KAIST, VMS Solutions

   • 2020.06.~2021.02.

10. Cyber-physical assembly and logistics systems in global supply chains

글로벌 공급 사슬의 조립 및 물류를 위한 사이버물리시스템

10. • 반자동 조립공정의 작업자 결근/이탈 상황에 대응 가능한 자동 대응 알고리즘 개발 및 빅데이터 분석 기반 설비 고장 예측 

    • by Python

    • with KAIST, SKKU, Dexta, Yura, KTH, Scania, Ericsson, H&D wireless (EUREKA program, MOTIE of Korea)

    • 2019.06.~2022.05.

11. Optimal weight sets for dispatching rules with multiple KPIs

다중 KPI를 고려한 디스패칭 룰 가중치 탐색 방법론 개발

11. • 스케줄 성능을 판별하는 지표가 다중인 경우 반도체 FAB 생산라인의 디스패칭 룰 기반 자동화 시스템의 최적 파라미터를 도출하기 위한 머신러닝 기반 알고리즘 개발 

    • by C# (MozArt), R

    • with KAIST, VMS Solutions

    • 2019.09.~2020.02.

12. Big data-based simulation and optimization technology for smart manufacturing

빅데이터기반 제조라인 물류운영 시뮬레이션 및 최적화 기술개발

12. • 배터리 생산 공정의 이상상황을 빅데이터 분석 방법을 통해 예측하고 이에 대응 가능한 스케줄링/리스케줄링 알고리즘 작동 프레임워크 제안

    • by Python

    • with SKKU, Micube solution, Samsung SDI, KITECH (MOTIE of Korea)

    • 2019.04.~2019.12. (entire 2019.04.~2020.12.)

13. Development of scheduling theory and algorithms with reinforcement learning for manufacturing systems

강화학습 기반 생산시스템 스케줄링 이론 및 알고리즘 개발

13. • 다양한 생산 시스템의 스케줄링을 위한 강화학습 기반 알고리즘 개발 및 성능 검토 

    • by Python (torch)

    • with SKKU, KAIST (NRF of Korea)

    • 2019.03.~2022.02.

14. Methodology for dispatching rules' weights

Dispatching rules 가중치 제안

14. • 반도체 FAB 생산라인의 디스패칭 룰 기반 자동화 시스템의 최적 파라미터 도출 

    • by C# (MozArt), R

    • with SKKU, SK Hynix

    • 2018.07.~2018.09.

15. Framework development for KPI analysis with various weights on dispatching rules

디스패칭 룰 가중치에 따른 KPI 변화 분석 프레임워크 개발

15. • 반도체 FAB 생산라인의 디스패칭 룰 기반 자동화 시스템의 최적 파라미터를 도출하기 위한 머신러닝 기반 알고리즘 개발 및 상관성 분석 

    • by C# (MozArt), R

    • with SKKU, VMS Solutions

    • 2018.07.~2019.02.

16. Analysis of KPIs according to weights on dispatching rules for LCD manufacturing

LCD 생산 공정 스케줄링을 위한 디스패칭 룰 가중치에 따른 KPI 변화 분석

16. • LCD FAB 생산라인의 디스패칭 룰 기반 자동화 시스템의 파라미터에 따른 스케줄 성능 변화를 체계적으로 분석할 수 있는 프레임워크 개발 

    • by C# (MozArt), R

    • with SKKU, VMS Solutions

    • 2017.07.~2018.02.

17. Design and analysis for operations optimizations of smart factory testbed

마이크로 스마트팩토리 테스트베드 가상공장 설계, 운영 분석을 통한 개선 및 최적화 방안 도출

17. • 3D 프린터 기반 고객 맞춤 제조 시스템의 유연적인 생산 자동화를 위한 스케줄링/리스케줄링 알고리즘 고도화 및 성능 검토

    • by Python

    • with SKKU, ETRI

    • 2017.07.~2018.01.

18. Development of scheduling and rescheduling algorithms for 3D printer-based smart factory

3D 프린터 기반 스마트팩토리 최적 스케줄링 및 이상상황 대응 리스케줄링 알고리즘 개발

18. • 3D 프린터를 활용하는 유연 생산 시스템의 운영 자동화 측면 이슈사항 정의 및 스케줄링/리스케줄링 알고리즘 개발 

    • by Python

    • with SKKU (NRF of Korea)

    • 2016.11.~2019.10.

19. Development of algorithms for detecting and improving inefficient schedules in LCD processes

LCD 생산공정의 스케줄링 문제 탐색 및 개선 알고리즘 개발

19. • LCD FAB 생산라인의 기존 공정 스케줄이 주어졌을 때 휴리스틱 알고리즘을 디자인하여 스케줄의 효율성 향상 도모 

    • by C# (MozArt)

    • with SKKU, VMS Solutions

    • 2016.07.~2017.02.

20. Development of open FaaS IoT service platform for mass personalization

개인맞춤 생산을 위한 고객-제조-유통 연계 개방형 FaaS IoT 서비스 플랫폼 기술 개발

20. • 3D 프린터 기반 고객 맞춤 제조 시스템의 유연적인 생산 자동화를 위한 스케줄링/리스케줄링 알고리즘 개발 

    • by Python

    • with SKKU, ETRI, Yonsei Univ., HyVISION system, Coever I&T, PartDB, Latis Global Communications (MSIP of Korea)

    • 2016.04.~2018.05. (entire 2015.06.~2018.05.)

• First Prize  -  Simulation Challenge  -  Gating Control in Semiconductor Fabrication  -  Winter Simulation Conference  -  San Antonio, USA, Dec. 2023.  ($3,000)

• Third Prize  -  AI Competition: Solving Real-world Problems  -  Integrated Planning Module using AI  -  Hankook & Company  -  Daejeon, Korea, Sep. 2023.  ($1,500)

• Second Prize  -  Poster Competition: Solving Industry/Social Problems  -  RL for Resource Leveling in Shipbuilding -  KAIST  -  Daejeon, Korea, Sep. 2022.

• Certificate of Appreciation  -  Successful Project  -  Workload Balancing of Ship Cargo Production  -  Samsung Heavy Industries  -  Korea, May. 2022.

• Ph.D. Candidate Research Incentive Support  -  National Research Foundation of Korea  -  May. 2022– Apr. 2023.  ($15,000)

• Third Prize  -  2015-2016 PACE RSMS Competition (Second Year): Manufacturing Engineering  -  General Motors  -  Cincinnati, USA, Jul. 2016.

• Third Prize  -  2015-2016 PACE RSMS Competition (Second Year): Customer Insight  -  General Motors  -  Cincinnati, USA, Jul. 2016.

• Optimizing method for the manufacturing process [Link]

공정 최적화 방법 

• • 반자동 조립 공정에서의 작업자 할당 실시간 최적화 프레임워크 개발 

  • Application date: 2021.05.31

  • Application number: KR1020210070359

  • Applicant: KAIST, Yura (entire 12 people)

  • Authority: Republic of Korea