SCC Programming Model for Crude Oil Scheduling and Storage Management

Abstract

A stochastic chance constrained mixed-integer nonlinear programming (SCC-MINLP) model is developed in this paper to solve the integrated problem of refinery short term crude oil scheduling, blending and storage management under demands uncertainty of crude oil distillation units (CDUs). It is the first time that the uncertain CDUs' demands in this problem are set as random variables which have discrete and continuous probability distributions. To reduce the computation complexity, the SCC-MINLP model is transformed into its equivalent stochastic chance constrained mixed-integer linear programming model (SCC-MILP) by using the method of reference. Stochastic simulation and stochastic sampling technologies are adopted to solve the complex SCC-MILP model. Finally, a case study which has 265 continuous variables, 68 binary variables and 318 constraints is effectively solved with the proposed approaches.