چكيده به لاتين
Through the recent decades, the detrimental effects of excessive use of fossil fuels and the problems caused by the growth of their consumption have become one of the most significant human concerns all around the world. Biodiesel provides more sustainable and renewable energy than conventional diesel fuel and improves engine efficiency. also, it prepares safer storage conditions and reduces national dependency on foreign suppliers. therefor, in this thesis, a resilient and robust supply chain network design under operational and disruption risks is developed to produce second-generation biodiesel from jatropha and waste cooking oil. The proposed two-stage stochastic scenario-based model minimizes system costs and networks non-resiliency and also maximizes social impacts by considering economic, social, and environmental aspects of sustainability. At the first stage, due to the determination of the most probable jatropha cultivation locations, the provinces of Iran were investigated based on jatropha characteristics by using the best-worst method and geographic information system. Entities of this supply chain include supply, intermediate extracting, processing, storage and distribution, and demand centers. The strategic decisions of this multi-product, multi-period, and multi-objective model include the determination of optimal cultivation, oil extraction, biorefineries, and storage and distribution centers, proper conversion technology, and biomass allocation. Operational and tactical decisions contain inventory control, flow management, and transportation mode. the effect of disruptions is considered on all constructed facilities from Jatropha cultivation centers to biodiesel storage and distribution centers. These disruptions are independent and could be complete or partial and preventive resiliency strategies, include reducing flow complexity, node complexity, and node criticality, are implemented to face their effects. The augmented ɛ-constraint method is applied to solve the multi-objective model and a robust approach is utilized to defeat uncertainties. Solving the model in different conditions shows a 9.46% improvement in the economic objective of the resilient model compared to the disturbed model when a disturbance occurs.
