Full metadata record
DC Field | Value | Language |
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dc.contributor.advisor | 박경준 | - |
dc.contributor.author | Jinyoung Kim | - |
dc.date.accessioned | 2022-07-07T02:29:09Z | - |
dc.date.available | 2022-07-07T02:29:09Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000363406 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/16677 | - |
dc.description.abstract | Multi-job production is a class of manufacturing systems that produce different products within the same production system. These systems are widely used in production assembly, and becoming a trend with smart factories. In this paper, we propose a Deep-Q reinforcement learning driven scheduling algorithm for multi-job production. In particular, we take into account machine breakdown and production plan change as the inputs of the learning process, which are typically considered as unexpected situations in previous studies. We validate the proposed scheme with real data collected for 6 months between May and October 2019 from a tier-1 vendor of a world top-4 motor company. Our case study shows that the proposed scheme improves the throughput of the production line by 37% compared to the conventional rigid method. | - |
dc.description.statementofresponsibility | Y | - |
dc.description.tableofcontents | Abstract List of Figures List of Tables Notation, Symbols, and Acronyms 1 Introduction 1 2 Related Work 3 3 Target Manufacturing System 5 3.1 Data Processing 5 3.1.1 Work Time 8 3.1.2 Machine Parameters 8 3.1.3 Buffers 10 3.2 Factory Description 11 3.3 Distinctive Feature of the Line 12 3.4 Scheduling Problem 12 4 Deep-Q Scheduling 14 4.1 Agent and Environment of RL 14 4.2 States, Actions, and Rewards 15 4.3 Training Method 17 5 Experimental Results 20 5.1 Data Sets and Training Details 20 5.2 Performance Evaluation 21 6 Conclusions 25 Bibliography 26 국문초록 29 |
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dc.format.extent | 30 | - |
dc.language | eng | - |
dc.publisher | DGIST | - |
dc.subject | Multi-job line, production scheduling, reinforcement learning, data processing, flexible production systems, 멀티잡 라인, 생산 스케줄링, 강화학습, 데이터 프로세싱, 유연한 생산 시스템 | - |
dc.title | DQN Learning Approach to Scheduling in Multi-job Production Systems | - |
dc.title.alternative | 멀티잡 생산라인 시스템에서의 Deep-Q 강화학습 기반 스케줄링 : 사례 연구 | - |
dc.type | Thesis | - |
dc.identifier.doi | 10.22677/thesis.200000363406 | - |
dc.description.alternativeAbstract | 인공지능, IoT, 5G, AR 등 기술들의 발전으로 인해 스마트 팩토리는 전세계적인 추세이며, 4차 산업혁명의 핵심 요소이다. 이에 따라 각 정부들은 다양한 정책을 앞다투어 발표하고 목표를 설정한다. 이 중 다품종 소량 생산은 산업계의 전반적인 트렌드로써 4차 산업혁명의 핵심 목표 중 하나이다. 이를 위한 다양한 생산품을 생산할 수 있는 복수 작업 생산시스템 (multi-job line) 이나 작업장의 구조나 예약 변경이 자유로운 유연한 생산 시스템 (flexible production systems) 이 각광받고 있다. | - |
dc.description.degree | Master | - |
dc.contributor.department | Information and Communication Engineering | - |
dc.contributor.coadvisor | Yongsoon Eun | - |
dc.date.awarded | 2021/02 | - |
dc.publisher.location | Daegu | - |
dc.description.database | dCollection | - |
dc.citation | XT.IM 김78 202102 | - |
dc.contributor.alternativeDepartment | 정보통신융합전공 | - |
dc.contributor.affiliatedAuthor | Jinyoung Kim | - |
dc.contributor.affiliatedAuthor | Kyung-Joon Park | - |
dc.contributor.affiliatedAuthor | Yongsoon Eun | - |
dc.contributor.alternativeName | 김진영 | - |
dc.contributor.alternativeName | Kyung-Joon Park | - |
dc.contributor.alternativeName | 은용순 | - |