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Title: Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty

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 NP-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.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [2]
  1. Mississippi State Univ., Mississippi State, MS (United States). Dept. of Industrial and Systems Engineering
  2. Idaho National Lab. (INL), Idaho Falls, ID (United States). Biofuels and Renewable Energy Technologies
Publication Date:
Report Number(s):
INL/JOU-16-39778
Journal ID: ISSN 0360-8352; PII: S036083521730253X
Grant/Contract Number:
AC07-05ID14517
Type:
Accepted Manuscript
Journal Name:
Computers and Industrial Engineering
Additional Journal Information:
Journal Volume: 110; Journal Issue: C; Journal ID: ISSN 0360-8352
Research Org:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE)
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; 29 ENERGY PLANNING, POLICY, AND ECONOMY; Depot; Feedstock; Sample average approximation; multi-purpose pellet processing depots; densified biomass; progressive hedging; rolling horizon heuristic
OSTI Identifier:
1407417

Quddus, Md Abdul, Ibne Hossain, Niamat Ullah, Mohammad, Marufuzzaman, Jaradat, Raed M., and Roni, Mohammad S.. Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty. United States: N. p., Web. doi:10.1016/j.cie.2017.06.001.
Quddus, Md Abdul, Ibne Hossain, Niamat Ullah, Mohammad, Marufuzzaman, Jaradat, Raed M., & Roni, Mohammad S.. Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty. United States. doi:10.1016/j.cie.2017.06.001.
Quddus, Md Abdul, Ibne Hossain, Niamat Ullah, Mohammad, Marufuzzaman, Jaradat, Raed M., and Roni, Mohammad S.. 2017. "Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty". United States. doi:10.1016/j.cie.2017.06.001. https://www.osti.gov/servlets/purl/1407417.
@article{osti_1407417,
title = {Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty},
author = {Quddus, Md Abdul and Ibne Hossain, Niamat Ullah and Mohammad, Marufuzzaman and Jaradat, Raed M. and Roni, Mohammad S.},
abstractNote = {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 NP-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.},
doi = {10.1016/j.cie.2017.06.001},
journal = {Computers and Industrial Engineering},
number = C,
volume = 110,
place = {United States},
year = {2017},
month = {6}
}