Policy-based reinforcement learning algorithm and its application to semiconductor fab dispatching rule = Policy 기반 강화학습 알고리즘 연구와 반도체 Fab Dispatching 규칙에 적용

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dc.contributor.advisorShin, Hayong-
dc.contributor.advisor신하용-
dc.contributor.authorKim, Namyong-
dc.date.accessioned2021-05-12T19:42:45Z-
dc.date.available2021-05-12T19:42:45Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=924240&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284296-
dc.description학위논문(박사) - 한국과학기술원 : 산업및시스템공학과, 2020.8,[iv, 77p :]-
dc.description.abstractThis paper deals with the scheduling optimization based on dispatching rules for the efficient operation of the semiconductor factory. The modern semiconductor plant has a large scale and complex structure. It requires a very high computational cost in the design of sophisticated scheduling. These problems make it difficult to apply popular scheduling methods such as mathematical formulation and meta-heuristic, which require a high computational cost, to semiconductor factory. Therefore, almost manufacturers have been designing the schedule using very simple dispatching rules. However, in order to be a more efficient operation, more sophisticated dispatching rules should be designed for the factory. Thus, in this paper, we proposed the reinforcement learning-based algorithms for the design of more effective and sophisticated dispatching rules. As a first study, we proposed the per-machine linear dispatching rule learning approach for different multi-machines using population-based search. As a second study, to achieve higher data efficiency, we proposed per-machine dispatching rule learning approach using policy gradient, in which actors are decentralized, and critic is centralized. As a third study, we proposed a hybrid algorithm that takes both the advantage of a policy gradient method and a population-based search. Experiments showed that the proposed methods have better performance or data efficiency than existing methodologies.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectDispatching Rule▼aReinforcement Learning▼aPolicy Gradient▼aEvolutionary Approach▼aHybrid Algorithm-
dc.subjectDispatching 규칙▼a강화학습▼aPolicy Gradient▼a진화론적 접근법▼a하이브리드 알고리즘-
dc.titlePolicy-based reinforcement learning algorithm and its application to semiconductor fab dispatching rule = Policy 기반 강화학습 알고리즘 연구와 반도체 Fab Dispatching 규칙에 적용-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :산업및시스템공학과,-
dc.contributor.alternativeauthor김남용-
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