Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach


A hybrid system dynamics-mathematical model for biodiesel supply chain optimization.

A new system dynamics model to simulate future market variables of biodiesel.

The system dynamics estimates key parameters of the mathematical model.

Land, water resource and technology limitations for biomass & biodiesel productions.

A scenario-based stochastic programming model to optimize micro variables.


Development of biofuels causes reduction of environmental pollution, but certain limitations affect their production. In this research, a hybrid system dynamics-mathematical programming approach is developed to design and plan a biodiesel supply chain from biomass fields to consumption markets. The supply chain faces limitations in biodiesel production. Water resource limitations for biodiesel production, land limitations for biomass procurement, and technological issues are the most important limitations considered in the system dynamics model. In addition, competition between fossil fuels and biodiesel is taken into account. The proposed methodology, firstly, estimates the most important parameters in biodiesel supply chain in a given planning horizon. Then, estimated parameters are used as inputs of the mathematical model and the optimal supply chain decisions are made by means of a stochastic mixed-integer programming model. Besides, a scenario-based approach is used to model the disruption risks for links and biomass fields. Finally, a numerical experiment is presented to show the applicability of the methodology according to some interviews with experts in Iran. Results demonstrate the potential appropriate market of biodiesel in Iran while several resource and technology limitations and environmental pollution avoid growth of biodiesel market. Moreover, a sensitivity analysis is performed on risk preferences of decision makers and government policies adopted to improve the biodiesel market.


  • Biodiesel supply chain;
  • Optimization;
  • System dynamics;
  • Stochastic mixed-integer programming;
  • Renewable energy

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