首页> 外文会议>Fuzzy Systems and Knowledge Discovery; Lecture Notes in Artificial Intelligence; 4223 >Chance Constrained Programming with Fuzzy Parameters for Refinery Crude Oil Scheduling Problem
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Chance Constrained Programming with Fuzzy Parameters for Refinery Crude Oil Scheduling Problem

机译:炼油厂调度问题的模糊参数机会约束规划。

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The main objective of this work is to put forward a chance constrained mixed-integer nonlinear fuzzy programming model for refinery short-term crude oil scheduling problem under demands uncertainty of distillation units. The model studied has characteristics of discrete events and continuous events coexistence, multistage, multiproduct, uncertainty and large scale. Firstly, the model is transformed into its equivalent fuzzy mixed-integer linear programming model by using the method of Quesada & Grossmann. Then the fuzzy equivalent model is changed into its crisp MILP model relies on the theory presented by Liu & Iwamura for the first time in this area. Finally, the crisp MILP model is solved in LINGO 8.0 based on time discretization. A case study which has 265 continuous variables, 68 binary variables and 318 constraints is effectively solved with the proposed solution approach.
机译:这项工作的主要目的是针对精馏装置需求不确定的情况,针对炼油厂短期原油调度问题,提出一个机会约束的混合整数非线性模糊规划模型。研究的模型具有离散事件和连续事件并存,多阶段,多产品,不确定性和大规模的特征。首先,利用Quesada&Grossmann方法将模型转化为等效的模糊混合整数线性规划模型。然后,基于Liu和Iwamura首次在该领域提出的理论,将模糊等效模型转换为清晰的MILP模型。最后,基于时间离散化在LINGO 8.0中求解了清晰的MILP模型。所提出的解决方案有效地解决了一个包含265个连续变量,68个二元变量和318个约束的案例研究。

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