A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions

Yazdanparast R., Jolai F., Pishvaee M., Keramati A.

Renewable Energy, vol.184, pp.799-819, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 184
  • Publication Date: 2022
  • Doi Number: 10.1016/j.renene.2021.11.081
  • Journal Name: Renewable Energy
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, Index Islamicus, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.799-819
  • Keywords: Drop-in biofuels, Biofuel supply chain, Two-stage stochastic programming, Conditional value-at-risk, Supply chain resilience
  • TED University Affiliated: No


© 2021 Elsevier LtdDrop-in biofuels, which are functionally equivalent to petroleum-based transportation fuels and fully compatible with existing petroleum infrastructure, have been widely used in developed countries in recent decades as an effective means of reducing the greenhouse gas (GHG) emissions emitted by fossil fuels. The most challenging obstacles to biofuel development are the widespread access to cheap fossil fuels, high costs of constructing biorefineries, and potential disruptions in the biofuel supply chain (SC). This paper proposes a practical optimization model for the development of drop-in biofuels using the existing petroleum infrastructure. The model takes potential supply and production disruptions into account, and investigates four proactive strategies: flexible supply contracts, infrastructure fortification, alternative production routes, and prepositioning of emergency inventory to improve overall SC resilience. Furthermore, a risk-averse two-stage stochastic program is developed to cope with uncertainty where a conditional value-at-risk (CVaR) is used as the risk measure. By integrating the planning and operational decisions in this decision-making framework, we explore the best available solutions to ensure both economic and environmental sustainability. In the end, the applicability of the proposed model is verified through a real-world case study. The results indicate that the proposed strategies are effective in minimizing logistical costs.