Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain − A logistic case study in Midwestern United States

- Multi-period stochastic MILP model for optimizing biomass-to- biofuel supply chains.
- Multiple objectives of maximizing profit, GHG emission reduction, and jobs creation.
- Multiple uncertainties in biomass supply, bioethanol demand and sale price.
- Evaluation of fixed vs. variable subsidy policy for encouraging biofuel production.
- Use of Sample Average Approximation algorithm along with Bender's decomposition.

This work proposes a multi-objective optimization model to design a sustainable multi-period second generation biomass-to-bioethanol supply chain under multiple uncertainties. The objective is to simultaneously maximize the economic, environmental, and social performance. The strategic decisions such as land allocation for switchgrass cultivation, biorefinery locations and capacities, and the biomass-to-bioethanol conversion pathway are determined for each planning period which are staggered across the entire planning horizon. The augmented ε-constraint method is used to trade-off among the competing objectives and to obtain feasible solutions that achieve desired levels of sustainability. In order to solve the proposed stochastic optimization model efficiently and effectively, this work proposes a solution approach involving sequential application of a modified Sample Average Approximation method and Benders decomposition. A case study is presented to demonstrate the effectiveness of the proposed mathematical model and its impact on land usage and sustainability.