10.1007/s40095-022-00513-5

A robust optimization approach for an integrated hybrid biodiesel and biomethane supply chain network design under uncertainty: case study

  • Talayeh Kalhor1,
  • Mohammad Sharifi*1,
  • Hossein Mobli1,
  1. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, IR

Published in Issue 2022-07-09

How to Cite

Kalhor, T., Sharifi, M., & Mobli, H. (2022). A robust optimization approach for an integrated hybrid biodiesel and biomethane supply chain network design under uncertainty: case study. International Journal of Energy and Environmental Engineering, 14(2 (June 2023). https://doi.org/10.1007/s40095-022-00513-5
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Abstract

Abstract To overcome the problems of environmental concerns and the depletion of fossil resources, as well as to improve energy security, renewable energy resources can be substituted for petroleum-based fuels. Transportation sector can benefit from the utilization of biodiesel and biomethane as suitable green fuels. This study proposes a multi-period mixed-integer linear programming model to manage and design the biofuel supply chain network from two high-potential energy crops. This novel two-stage stochastic programming model aims to minimize overall cost as well as the environmental impacts of transportation and biofuel production. To manage the uncertainty in the biofuel supply chain network, a robust optimization approach is applied to hedge against demand uncertainty. To deal with the bi-objective programming model, an interactive fuzzy approach is employed. The usefulness of the model is illustrated by a real case study in Iran. Finally, the computational results are evaluated and discussed. Furthermore, a sensitivity analysis is performed on parameters that address decision-makers’ preferences and environmental concerns in the biofuel production system.

Keywords

  • Biofuel supply chain,
  • Uncertainty,
  • Robust optimization,
  • Interactive fuzzy approach

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