文献类型：期刊文献

英文题名：Stochastic chance constrained mixed-integer nonlinear programming models and the solution approaches for refinery short-term crude oil scheduling problem

作者：Cao, Cuiwen[1,2];Gu, Xingsheng[1];Xin, Zhong[2]

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Chem Engn, Shanghai 200237, Peoples R China

年份：2010

卷号：34

期号：11

起止页码：3231

外文期刊名：APPLIED MATHEMATICAL MODELLING

收录：;EI(收录号：20102212978583);WOS:【SCI-EXPANDED(收录号:WOS:000278842000004)】；

基金：Financial support from the National Natural Science Foundation of China (No.60774078), the National High Technology Research and Development Program of China (No. 2009AA04Z141), the China Postdoctoral Science Foundation funded project (No. 20080430080), Shanghai Commission of Nature Science (No. 10ZR1408300), the Research Fund for Outstanding Youth Teachers of East China University of Science and Technology (No.YH0157117) and the Fund for Shanghai Leading Academic Discipline Project (No.B504) is gratefully appreciated.

语种：英文

外文关键词：Uncertainty; Stochastic chance constrained; MINLP; Short-term crude oil scheduling problem; Discrete/continuous joint probability distributions; Stochastic simulation

摘要：Stochastic chance constrained mixed-integer nonlinear programming (SCC-MINLP) models are developed in this paper to solve the refinery short-term crude oil scheduling problem which concerns crude oil unloading, mixing, transferring and multilevel inventory control under demands uncertainty of distillation units. The objective of these models is the minimum expected value of total operation cost. It is the first time that the uncertain demands of Crude oil Distillation Units (CDUs) in these problems are set as random variables which have discrete and continuous joint probability distributions. This situation is close to the real world industry use. To reduce the computation complexity, these SCC-MINLP models are transformed into their equivalent stochastic chance constrained mixed-integer linear programming models (SCC-MILP). Stochastic simulation and stochastic sampling technologies are introduced in detail to solve these complex SCC-MILP models. Finally, case studies are effectively solved with the proposed approaches. (C) 2010 Elsevier Inc. All rights reserved.