首页|New Findings from East China University of Science and Technology in the Area of Computational Intelligence Reported (Knowledgeassisted Dual-stage Evolutionary Optimization of Large-scale Crude Oil Scheduling)
New Findings from East China University of Science and Technology in the Area of Computational Intelligence Reported (Knowledgeassisted Dual-stage Evolutionary Optimization of Large-scale Crude Oil Scheduling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning-Computational Intelligence. According to news reporting originating in Shanghai, People's Republic of China, by NewsRx journalists, research stated, “With the scaling up of crude oil scheduling in modern refineries, large-scale crude oil scheduling problems (LSCOSPs) emerge with thousands of binary variable s and non-linear constraints, which are challenging to be optimized by tradition al optimization methods. To solve LSCOSPs, we take the practical crude oil sched uling from a marine-access refinery as an example and start with modeling LSCOSP s from crude unloading, transportation, crude distillation unit processing, and inventory management of intermediate products.”
ShanghaiPeople's Republic of ChinaAsiaComputational IntelligenceMachine LearningEast China University of Scien ce and Technology
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