Robust Optimization On Sustainable Biofuel Supply Chain Under Uncertainties

Biofuels have been highly supported by the Chinese government, for its promising potiential on the emission reduction of transportation. However, the industry still needs further development due to lots of obstacles. The coordination problem, especially in uncertain environment, is the main non-technical problem of biofuel supply chain. Therefore, the Waste cooking oils (WCOs)-for-biodiesel industry, one of promising biofuel industry, is taken as the study object, and then the robust optimization on such a supply chain is studied.Firstly, the basic framework for robust optimization of a sustainable biodiesel supply chain under uncertainty is proposed from five dimensions, namely the characteristics, decisions, sustainbility, uncertainty and robustness of the supply chain. Based on this framework, the two typical modes, namely "Suzhou Jiejing"mode and "Changzhou Kate" mode, are analyzed.Secondly, the "Suzhou Jiejing" mode for collection and convernsion of waste cooking oils in the cities is taken as the study object. A robust muti-objetive mixed interger optimization model with biodiesel price uncertainty is proposed for facility locations and transportation plans, and then the Ben-Tal model is appied to transform the model into linear one. After that, the model is solved with K-means algorithm and CPLEX optimization software.Thirdly, the "Changzhou Kate" mode for collection and convernsion of waste cooking oils in big regions is taken as the study object. A robust muti-objetive mixed interger optimization model with uncertainties described as scenarios is proposed for facility locations and transportation plans, and then the Mulvey model is appied to transform the model into linear one. After that, an algorithm based on genetic algorithm is proposed and the CPLEX optimization software is applied to solve the model.Finally, these two models are applied in Suzhou and Yangtze River Delta for case study, respectively, and then the supply chain decisions, uncertainties as well as robustness are discussed.