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Biofuel logistics network scheme design with combined data envelopment analysis approach

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  • Hong, Jae-Dong
  • Mwakalonge, Judith L.

Abstract

This paper studies the biofuel logistics network scheme (BLNS) design problem dealing with several decision variables, such as the biofuel facility location, allocation, delivering harvested biomass stocks to biofuel producing facilities, and distributing biofuels to the stations. Since building the biofuel facilities requires a huge amount of investments, designing an efficient BLNS will be essential to attract potential investors. We formulate the design problem as a multi-objective programming (MOP) model. Solving the MOP model would yield various network schemes based upon the weight given to each objective/goal. Data envelopment analysis (DEA) method could be applied to evaluate the efficiency of each BLNS generated by solving the MOP model. Several approaches based on the traditional DEA method emerge to overcome a critical weakness regarding discriminating power. This paper combines three well-known DEA methods to make the most use of each method’s strengths and to evaluate and identify the more efficient network schemes. Through a case study for South Carolina, we observe that the proposed procedure performs well in terms of designing efficient and robust BLNSs. The proposed procedure would enable the decision-makers to have more choices for BLNSs to consider before finally selecting the best scheme regarding efficiency, practicality, and feasibility.

Suggested Citation

  • Hong, Jae-Dong & Mwakalonge, Judith L., 2020. "Biofuel logistics network scheme design with combined data envelopment analysis approach," Energy, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220314493
    DOI: 10.1016/j.energy.2020.118342
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    5. Cabrera-Jiménez, Richard & Mateo-Sanz, Josep M. & Gavaldà, Jordi & Jiménez, Laureano & Pozo, Carlos, 2022. "Comparing biofuels through the lens of sustainability: A data envelopment analysis approach," Applied Energy, Elsevier, vol. 307(C).

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