Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/13207
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dc.contributor.authorSalleh, S.F.en_US
dc.contributor.authorGunawan, M.F.en_US
dc.contributor.authorZulkarnain, M.F.B.en_US
dc.contributor.authorShamsuddin, A.H.en_US
dc.contributor.authorAbdullah, T.A.R.T.en_US
dc.date.accessioned2020-02-03T03:31:05Z-
dc.date.available2020-02-03T03:31:05Z-
dc.date.issued2019-
dc.identifier.urihttp://dspace.uniten.edu.my/jspui/handle/123456789/13207-
dc.description.abstractBiomass utilization in the generation of bioenergy and biofuels is promoted as a sustainable solution to decarbonise the energy sector. Effective management of biomass supply chain is pivotal to successful deployment of this energy-rich resource in the power sector. Using a case study in Malaysia where biomass feedstock sourcing problem is prevalent, a biomass supply chain system was proposed. Three important terminals were identified, namely; i) biomass supply point (BSP), where raw biomass materials are collected, ii) biomass processing facility (BPF), as the collection hub cum centralized conversion centre, and iii) biomass demand centre (BDC), where the processed biomass meets the end-use sectors. A new modelling approach was proposed to optimize the new BPF location with the objective to minimize total road travelling distance between each terminal. The results were compared with the other commonly used methods, namely centre of gravity and fuzzy clustering. The positive contributions of the proposed model in minimizing the overall logistic cost and CO2 emissions due to fuel consumption were discussed. © 2019, Dorma Journals. All rights reserved.
dc.language.isoenen_US
dc.titleModelling and optimization of biomass supply chain for bioenergy productionen_US
dc.typeArticleen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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