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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
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ISSN:0307-904X
DOI:10.1016/j.apm.2010.02.015