scenario-based robust optimization

Concerning global warming and the Greenhouse gas (GHG) effect, clean energy resources have captured researchers' interest recently. Biomass materials are among important biofuels and bioenergy production resources that have the potential to replace fossil fuels. Using biomass materials leads to a decline in GHG emission and air pollution levels, not being dependent on fossil fuels, and provide energy security. Due to the importance of bioenergy and biofuels, a multi-product, multi-period, and green mathematical model has been developed to improve economic and environmental objectives for bioethanol and the electricity supply chain. It includes the following decisions: determining production centers' location and capacity, technology selection, determining inventory holding level, biomass type selection, allocation, amount of material flow, and determining transportation modes. In this study, a scenario-based robust compromise programming approach (SRCP) is developed for the bi-objective solution of the provided mathematical model and determining Pareto optimal points under uncertain conditions. Finally, the performance and effectiveness of SRCP are provided, and the results obtained from the case study in Iran are analyzed. According to the results, Annual electricity and bioethanol production capacity are at least 8000 million kWh and 1250 kton, respectively, satisfying 10% of electricity and 5% of gasoline demand in 6 provinces of Iran. The sensitivity analysis also shows that equal weight for both objectives can be more logical for decision makers.

Data Envelopment Analysis (DEA) is one of the popular and applicable techniques for assessing and ranking the stocks or other financial assets. It should be noted that in the financial markets, most of the times, the inputs and outputs of DEA models are accompanied by uncertainty. Accordingly, in this paper, a novel Robust Data Envelopment Analysis (RDEA) model, which is capable to be used in the presence of discrete and continuous uncertainties, is presented. The proposed novel RDEA model in the paper was implemented in a real case study of Tehran Stock Exchange (TSE). The results showed that the proposed new RDEA model was effective in the assessment and ranking of the stocks under different scenarios with interval values.