Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

“…The data on the distances, transportation, and occupied land cost data were obtained from a previous study. 9 Based on the life expectancy of 15 years, this study investigates the effect of seasonality on the supply chains by examining 12 time periods per year. Three major types of biomass were considered in this study to meet 10% of gasoline demand by ethanol (E10): seaweed (third-generation biofuel feedstock), pellets, and wood chips (second-generation biofuel feedstock).…”

“…18 A bed drying process was used to reduce the moisture in raw seaweed from 85% to 15%, with a capacity of 7.5 million dry tons per year. 9 The remaining parameters in the supply chain were obtained from a previous study. 9 Moreover, the values of the environmental parameters can be found in the GREET model 26 and relevant literature.…”

“…9 The remaining parameters in the supply chain were obtained from a previous study. 9 Moreover, the values of the environmental parameters can be found in the GREET model 26 and relevant literature. 27…”

“…4 A biofuel supply chain should be designed such that materials, information, and financial flows are efficiently managed. 9 Until now, most of the studies have been focused on building a cost-effective biofuel supply chain through oneobjective optimization problems based on maximizing social surplus or minimizing cost. 10 However, the fact that biofuel production mitigates environmental problems such as global warming and air pollution should not be ignored.…”

Towards facing uncertainties in biofuel supply chain networks: a systematic literature review

“…This methodology assumes that the probability of the distribution function is available (Geismar et al 2021 ; Lo et al 2023 ) or at least estimable in advance (Díaz-Trujillo et al 2020 ; Osmani and Zhang 2014b ), which can be continuous (Castillo-Villar et al 2017 ; Ye et al 2017 ) or scenario-based (Gonela et al 2015a ; Zarei et al 2022 ). When the problem is dealing with a continuous distribution function, only one uncertain parameter is usually involved, and the estimation and analysis are carried out according to the datasets and historical data.…”