Multi-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming

Cited 14 time in webofscience Cited 11 time in scopus
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dc.contributor.authorShin, Joohyunko
dc.contributor.authorLee, Jay Hyungko
dc.date.accessioned2019-04-15T14:32:08Z-
dc.date.available2019-04-15T14:32:08Z-
dc.date.created2019-03-26-
dc.date.issued2019-02-
dc.identifier.citationCOMPUTERS & CHEMICAL ENGINEERING, v.121, pp.556 - 573-
dc.identifier.issn0098-1354-
dc.identifier.urihttp://hdl.handle.net/10203/254142-
dc.description.abstractThis study focuses on the linkage between decision layers that have different time scales. The resulting expansion of the boundary of decision-making process can provide more robust and flexible management and operation strategies by resolving inconsistencies between different levels. For this, we develop a multi-timescale decision-making model that combines Markov decision process (MDP) and mathematical programming (MP) in a complementary way and introduce a computationally tractable solution algorithm based on reinforcement learning (RL) to solve the MP-embedded MDP problem. To support the integration of the decision hierarchy, a data-driven uncertainty prediction model is suggested which is valid across all time scales considered. A practical example of refinery procurement and production planning is presented to illustrate the proposed method, along with numerical results of a benchmark case study. (C) 2018 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.titleMulti-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming-
dc.typeArticle-
dc.identifier.wosid000460730900042-
dc.identifier.scopusid2-s2.0-85057534192-
dc.type.rimsART-
dc.citation.volume121-
dc.citation.beginningpage556-
dc.citation.endingpage573-
dc.citation.publicationnameCOMPUTERS & CHEMICAL ENGINEERING-
dc.identifier.doi10.1016/j.compchemeng.2018.11.020-
dc.contributor.localauthorLee, Jay Hyung-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorMulti-timescale decision making-
dc.subject.keywordAuthorDecision under uncertainty-
dc.subject.keywordAuthorMarkov decision process-
dc.subject.keywordAuthorMathematical programming-
dc.subject.keywordAuthorReinforcement learning-
dc.subject.keywordPlusSCHEDULING PROBLEMS-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusUNCERTAINTY-
dc.subject.keywordPlusFORMULATION-
dc.subject.keywordPlusFRAMEWORK-
dc.subject.keywordPlusSTRATEGY-
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