Izvorni znanstveni članak
https://doi.org/10.30765/er.40.3.11
Location optimization of biodiesel processing plant based on rough set and clustering algorithm - a case study in China
Nana Geng
; School of Transportation, Southeast University, Nanjing, 210018, China
Yong Zhang
; School of Transportation, Southeast University, Nanjing, 210018, China
Yixiang Sun
; School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
Sažetak
Biofuel has an important role in alleviating the environmental pollution problem. More attention has been paid to optimization of biofuel supply chain in recent years. In this paper, a scientific, rational and practical biodiesel processing plant location with waste oil as the raw material was proposed in order to provide a theoretical basis for guiding the planning and management of restaurants, waste oil collection points, and processing plants. Considering the merits and demerits of the subjective and objective weighting methods, this paper proposes a new weighting method which is namely the combination of rough set theory and clustering algorithm. It then verifies the location results with a plant carbon emission. At last, this paper analyzes the location of biodiesel processing plant in the Yangtze River Delta of China and finds that the precision has been greatly improved with the new method comparing the RMSE and the R2 of the Delphi method with the improved rough set theory. By using this method, the weights of the influencing factors of biodiesel processing plants are the following: Waste oil supply 0.143, Fixed construction cost factor 0.343, Biodiesel demand 0.143 and Location convenience 0.371. In the comparison between the robust optimization method and the improved rough set theory, it was found that the final location results are the same, all being Jiaxing City. However, the improved rough set theory is much simpler than the robust optimization algorithm in the calculation process.
Ključne riječi
biofuel; supply chain; rough set theory; clustering algorithm
Hrčak ID:
238271
URI
Datum izdavanja:
21.5.2020.
Posjeta: 964 *