Dynamic job shop scheduling in human-machine manufacturing systems: Deep reinforcement learning approach
- Title
- Dynamic job shop scheduling in human-machine manufacturing systems: Deep reinforcement learning approach
- Other Titles
- 심층강화학습을 이용한 인간-기계 제조시스템의 동적 스케줄링
- Author
- Taejong Joo
- Alternative Author(s)
- 주태종
- Advisor(s)
- 신동민
- Issue Date
- 2018-02
- Publisher
- 한양대학교
- Degree
- Master
- Abstract
- This thesis concerns job shop scheduling problem in human-machine manufacturing systems. A key aspect of this problem is to carefully consider the characteristics of human operators. For instance, every human has different work preferences and abilities, and these properties can be important information on job allocation. Despite its’ importance, few studies have investigated the characteristics of human operators in job-shop scheduling problem. One of the major reasons for lack of the research is that the characteristics of human operators are usually hidden in nature, which makes the mathematical formulation of the scheduling problem difficult. To tackle this challenge, this thesis proposes deep reinforcement learning approach to implicitly capture the unobservable characteristics of the human operators. Numerical experiments demonstrate the proposed model can consider task competence level and fatigue development of individual human operators which are hidden but implicitly contained in accumulated historical data. The result that the proposed method gives the shortest flowtime compared to classical dispatching rules supports usefulness of considering the characteristics human operators in a job shop scheduling problem.
- URI
- https://repository.hanyang.ac.kr/handle/20.500.11754/68158http://hanyang.dcollection.net/common/orgView/200000431995
- Appears in Collections:
- GRADUATE SCHOOL[S](대학원) > INDUSTRIAL MANAGEMENT ENGINEERING(산업경영공학과) > Theses (Master)
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