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    A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty

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    Author
    Babazadeh, Reza
    Ghaderi, Hamid
    Pishvaee, Mir Saman
    Affiliation
    Univ Arizona, Dept Syst & Ind Engn
    Issue Date
    2019-05-08
    Keywords
    Bioenergy
    Biofuel supply chain
    Optimization
    Benders Decomposition
    Uncertainty
    
    Metadata
    Show full item record
    Publisher
    PERGAMON-ELSEVIER SCIENCE LTD
    Citation
    Babazadeh, R., Ghaderi, H., & Pishvaee, M. S. (2019). A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty. Computers & Chemical Engineering, 124, 364-380.
    Journal
    COMPUTERS & CHEMICAL ENGINEERING
    Rights
    © 2019 Elsevier Ltd. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    This paper proposes a possibilistic programming model in order to design a second-generation biodiesel supply chain network under epistemic uncertainty of input data. The developed model minimizes the total cost of the supply chain from supply centers to the biodiesel and glycerin consumer centers. Waste cooking oil and Jatropha plants, as non-edible feedstocks, are considered for biodiesel production. To cope with the epistemic uncertainty of the parameters, a credibility-based possibilistic programming approach is employed to convert the original possibilistic programming model into a crisp counterpart. An accelerated benders decomposition algorithm using efficient acceleration mechanisms is devised to deal with the computational complexity of solving the proposed model in an efficient manner. The performance of the proposed possibilistic programming model and the efficiency of the developed accelerated benders decomposition algorithm are validated by performing a computational analysis using a real case study in Iran. (C) 2019 Elsevier Ltd. All rights reserved.
    Note
    12 month embargo; published online: 24 January 2019
    ISSN
    00981354
    DOI
    10.1016/j.compchemeng.2019.01.013
    Version
    Final accepted manuscript
    Sponsors
    Iran National Science Foundation (INSF)
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0098135418305246
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.compchemeng.2019.01.013
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