A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities
Research Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/11/9/968 |
| _version_ | 1797554522942341120 |
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| author | Sam Van Holsbeeck Sättar Ezzati Dominik Röser Mark Brown |
| author_facet | Sam Van Holsbeeck Sättar Ezzati Dominik Röser Mark Brown |
| author_sort | Sam Van Holsbeeck |
| collection | DOAJ |
| description | Research Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in designing a bioenergy supply chain at the strategic planning level. The result of this analysis can affect supply chain costs and the overall efficiency of the network, due to the low density and dispersed nature of forest biomass and the high costs associated with its logistics operations. In this study, we develop a two-stage decision support system to identify the optimal site locations for forest biomass conversion based on biomass availability, transport distance and cost. Materials and Methods: In the first stage, a GIS-based analysis is designed to identify strategic locations of potential bioenergy sites. The second stage evaluates the most cost-effective locations individually using a transportation cost model, based on the results from stage one. The sensitivity of inputs, such as maximum allowable transport cost, the distance of transport and their relations to the profit balance, and changes in fuel price are tested. The method is applied to a real case study in the state of Queensland, Australia. Results and Conclusions: The GIS analysis resulted in 128 strategic candidate locations being suggested for bioenergy conversion sites. The logistics analysis estimated the optimal cost and transportation distance of each one of the locations and ranked them according to the overall performance between capacities of 5 and 100 MW. |
| first_indexed | 2024-03-10T16:33:22Z |
| format | Article |
| id | doaj.art-e82f03a97ba14c84958022918607fa70 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T16:33:22Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-e82f03a97ba14c84958022918607fa702023-11-20T12:41:35ZengMDPI AGForests1999-49072020-09-0111996810.3390/f11090968A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy FacilitiesSam Van Holsbeeck0Sättar Ezzati1Dominik Röser2Mark Brown3Forest Research Institute, University of the Sunshine Coast, Locked Bag 4, Maroochydore, QLD 4558, AustraliaDépartement de Génie Mécanique, Université Laval, Québec City, QC G1V 0A6, CanadaDepartment of Forest Resources Management, Faculty of Forestry, University of British Columbia, MainMall 2424, Vancouver, BC V6T 1Z4, CanadaForest Research Institute, University of the Sunshine Coast, Locked Bag 4, Maroochydore, QLD 4558, AustraliaResearch Highlights: A set of 128 potential bioenergy facility locations is established and evaluated based on the transport cost to select optimal locations. Background and Objectives: The identification of optimal facility locations to process recovered forest biomass is an important decision in designing a bioenergy supply chain at the strategic planning level. The result of this analysis can affect supply chain costs and the overall efficiency of the network, due to the low density and dispersed nature of forest biomass and the high costs associated with its logistics operations. In this study, we develop a two-stage decision support system to identify the optimal site locations for forest biomass conversion based on biomass availability, transport distance and cost. Materials and Methods: In the first stage, a GIS-based analysis is designed to identify strategic locations of potential bioenergy sites. The second stage evaluates the most cost-effective locations individually using a transportation cost model, based on the results from stage one. The sensitivity of inputs, such as maximum allowable transport cost, the distance of transport and their relations to the profit balance, and changes in fuel price are tested. The method is applied to a real case study in the state of Queensland, Australia. Results and Conclusions: The GIS analysis resulted in 128 strategic candidate locations being suggested for bioenergy conversion sites. The logistics analysis estimated the optimal cost and transportation distance of each one of the locations and ranked them according to the overall performance between capacities of 5 and 100 MW.https://www.mdpi.com/1999-4907/11/9/968forest biomassbioenergylogistics costoptimal facility locationbiomass utilization |
| spellingShingle | Sam Van Holsbeeck Sättar Ezzati Dominik Röser Mark Brown A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities Forests forest biomass bioenergy logistics cost optimal facility location biomass utilization |
| title | A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities |
| title_full | A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities |
| title_fullStr | A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities |
| title_full_unstemmed | A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities |
| title_short | A Two-Stage DSS to Evaluate Optimal Locations for Bioenergy Facilities |
| title_sort | two stage dss to evaluate optimal locations for bioenergy facilities |
| topic | forest biomass bioenergy logistics cost optimal facility location biomass utilization |
| url | https://www.mdpi.com/1999-4907/11/9/968 |
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