期刊
SUSTAINABILITY
卷 14, 期 20, 页码 -出版社
MDPI
DOI: 10.3390/su142013675
关键词
bi-objective mixed integer programming; renewable energy; green; robust possibilistic approach; biofuel supply chain network design
This paper presents a new mathematical model aiming to minimize CO2 emission and total costs in the biofuel supply chain, creating a green supply chain network. Uncertain data regarding CO2 emission and biofuel demand were addressed using a robust possibilistic programming approach. The applicability and performance of the model were demonstrated based on an experimental example.
In recent years, concerns regarding issues such as climate change, greenhouse gas emissions, fossil reserve dependency, and petroleum price fluctuation have led countries to focus on renewable energies. Meanwhile, in developing countries, designing an appropriate biofuel supply chain network regarding environmental competencies is an important problem. This paper presents a new bi-objective mixed integer mathematical model aiming to minimize CO2 emission and total costs in the process of the biofuel supply chain, creating a suitable green supply chain network. In this respect, CO2 emission and biofuel demand are regarded as uncertain data to address the real complex cases. Moreover, the SAUGMECON approach was implemented to construct a single objective model, and the obtained Pareto optimal points were depicted and analyzed. Thereby, a robust possibilistic programming approach was implemented to the proposed model to handle existing imprecise data. Furthermore, the applicability and performance of the proposed model were demonstrated based on an experimental example. In this respect, the obtained results from the proposed robust possibilistic programming model were compared with its crisp form to show the robustness and reliability of the proposed uncertain mathematical model.
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