Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty [electronic resource]
Depot; Feedstock; Sample Average Approximation; Multi-Purpose Pellet Processing Depots; Densified Biomass; Progressive Hedging; Rolling Horizon Heuristic.
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| Online Access: |
Online Access (via OSTI) |
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| Corporate Author: | |
| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Washington, D.C. : Oak Ridge, Tenn. :
United States. Office of the Assistant Secretary for Nuclear Energy ; distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2017.
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| Subjects: |
| Summary: | Depot; Feedstock; Sample Average Approximation; Multi-Purpose Pellet Processing Depots; Densified Biomass; Progressive Hedging; Rolling Horizon Heuristic. |
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| Abstract: | This work develops a two-stage stochastic mixed-integer programming model to manage multi- purpose pellet processing depots under feedstock supply uncertainty. The proposed optimization model would help to minimize cost and to mitigate emissions from the supply chain network. We consider three alternative Biomass Processing and Densi cation Depot (BPDD) technologies; namely, conventional pellet processing, high moisture pellet processing, and ammonia ber expansion. These three technologies pre-process/pre-treat and densify different types of biomass into more highly densi ed intermediate products for different markets in order to improve movability and overall supply network performance in terms of costs and emissions. A hybrid decomposition algorithm was developed that combines sample average approximation with an enhanced Progressive Hedging (PH) algorithm to solve this challenging <em>NP</em>-hard problem. Some heuristics such as Rolling Horizon (RH) heuristic, variable xing technique were later applied to further enhance the PH algorithm. Mississippi and Alabama were selected as a testing ground and ArcGIS was employed to visualize and validate the modeling results. The results of the analysis reveal promising insights that could lead to recommendations to help decision makers achieve a more cost-effective environmentally-friendly supply chain network. |
| Item Description: | Published through SciTech Connect. 06/10/2017. "inl/jou--16-39778" ": S036083521730253X" Computers and Industrial Engineering 110 C ISSN 0360-8352 AM. Md Abdul Quddus; Niamat Ullah Ibne Hossain; Marufuzzaman Mohammad; Raed M. Jaradat; Mohammad S. Roni. |
| Physical Description: | p. 462-483 : digital, PDF file. |