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Location Allocation of Corn Stover Pretreatment Facilities in South Korea Under an Agent-Based Simulation Framework

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dc.contributor.authorKim, Youngjin-
dc.contributor.authorSeo, Junyoung-
dc.contributor.authorKim, Sojung-
dc.date.accessioned2025-09-25T05:00:06Z-
dc.date.available2025-09-25T05:00:06Z-
dc.date.issued2025-08-
dc.identifier.issn2076-3417-
dc.identifier.issn2076-3417-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/61599-
dc.description.abstractThis research proposes a novel location allocation framework that utilizes agent-based simulation for the efficient production of corn stover-based bioethanol, which requires dedicated pretreatment facilities for the feedstock. The framework comprises two main modules: (1) a Pretreatment Facility Module that assesses the performance of the corn stover-based bioethanol supply chain based on the interactions among three types of agents, namely order agent, pretreatment agent, and transport agent, and (2) an Optimization Module designed to determine the optimal supply chain configuration by selecting the most suitable number and locations for pretreatment facilities to achieve the lowest total operational cost. The framework is implemented in a case study for South Korea, which aims to raise the bioethanol blending ratio from 4% in 2025 to 8% by 2030. Experimental results reveal that, within the bioethanol supply chain comprising eight farms and four refineries, a 1% increase in bioethanol blending ratio leads to an increase in the demand for approximately 2229 kL of ethanol (10,225 tons of corn stover), and the proposed framework enables to identify the optimal location of pretreatment facilities in the subject supply chain according to the change in ethanol demand.-
dc.format.extent23-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleLocation Allocation of Corn Stover Pretreatment Facilities in South Korea Under an Agent-Based Simulation Framework-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/app15179488-
dc.identifier.scopusid2-s2.0-105015478120-
dc.identifier.wosid001569596900001-
dc.identifier.bibliographicCitationApplied Sciences, v.15, no.17, pp 1 - 23-
dc.citation.titleApplied Sciences-
dc.citation.volume15-
dc.citation.number17-
dc.citation.startPage1-
dc.citation.endPage23-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryEngineering, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusLIGNOCELLULOSIC BIOMASS-
dc.subject.keywordPlusBIOFUEL PRODUCTION-
dc.subject.keywordPlusSUPPLY CHAINS-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordPlusMICROALGAE-
dc.subject.keywordPlusSYSTEM-
dc.subject.keywordAuthoragent-based simulation-
dc.subject.keywordAuthorrenewable energy-
dc.subject.keywordAuthorcorn stover-
dc.subject.keywordAuthorsupply chain-
dc.subject.keywordAuthorlocation allocation-
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