A two-stage multi-objective second generation biodiesel supply chain design considering social sustainability: A case study. (15th October 2021) - Mohtashami, Zahra; Bozorgi-Amiri, Ali; Tavakkoli-Moghaddam, Reza
Abstract: Undesirable environmental impacts caused by fossil fuel consumption have motivated governments to focus on biofuels production. Therefore, in our paper, we design a multi-objective biodiesel supply chain from Jatropha, which includes biomass cultivation center, pretreatment, and oil extraction center, biorefinery, and demand zone. In our study, a two-stage approach presented, which first specifies candidate locations for biomass cultivation with a common weight data envelopment analysis (CWDEA) method and then strategic and tactical decisions were made by a mathematical model. The presented multi-objective model focuses on cost minimization, besides social benefits and environmental impacts maximization while a life cycle assessment approach is used to model social indicators. The capability of the model is validated by proposing a case study of Iran and the augmented ε-constraint method is employed to reach a trade-off between three pillars of sustainability while managerial insights are provided by employing a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for the optimal solution selection. Model's results and sensitivity analysis on key parameters, specify the best locations for Jatropha Curcas L. (JCL) cultivation and facility construction as well as an optimal value of supply chain variables, which finally proves that Iran has a high potential for biodiesel production from JCL that leads improving balanced socio-economic development in this country. Highlights: Presenting a two-stage approach for designing sustainable biodiesel supply chain. Quantifying social criteria in a multi-objective biodiesel supply chain. Cost optimization by specifying the best locations for feed stock cultivation. Presenting the trade-off between three pillars of sustainability. Considering decision-makers' preferences for selecting the optimum solution.