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

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 distil...

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Veröffentlicht in:	Applied mathematical modelling Jg. 34; H. 11; S. 3231 - 3243
Hauptverfasser:	Cao, Cuiwen, Gu, Xingsheng, Xin, Zhong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Inc 01.11.2010 Elsevier
Schlagworte:	Applied sciences Constraints Crude oil Crude oil, natural gas and petroleum products Demand Discrete/continuous joint probability distributions Energy Exact sciences and technology Fuels Inventory control, production control. Distribution Marketing Mathematical models Mathematical programming MINLP Nonlinear programming Operational research and scientific management Operational research. Management science Processing of crude oil and oils from shales and tar sands. Processes. Equipment. Refinery and treatment units Refineries Scheduling, sequencing Short-term crude oil scheduling problem Stochastic chance constrained Stochastic simulation Stochasticity Uncertainty MINLP Stochastic simulation Uncertainty Stochastic chance constrained Discrete/continuous joint probability distributions Short-term crude oil scheduling problem Short term Stochastic model Costs Mixing Probability distribution Modeling Linear model Uncertain system Crude oil Inventory control Sampling Random variable Distillation Probabilistic approach Unloading Mixed integer programming Scheduling Refinery Joint distribution Computational complexity distributions Oil industry Supply demand balance Non linear model Discrete/continuous joint probability Random set
ISSN:	0307-904X
Online-Zugang:	Volltext
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