Credibility Fuzzy Chance Constrained Models for Crude Oil Scheduling and Storage Management

Abstract

Three kinds of credibility fuzzy chance constrained mixed-integer nonlinear programming models (CFCC-MINLP) and the solution methods for the integrated problem of refinery short-term crude oil scheduling and storage management under demands uncertainty of CDUs (Crude-oil Distillation Units) are developed in this paper. To reduce the calculation complexity of these models, they are transformed into their equivalent fuzzy chance constrained mixed-integer linear programming (CFCC-MILP) models. After that these CFCC-MILP models are solved through their crisp equivalent algorithms based on the theory presented by Liu & Liu [2002] for the first time in refinery scheduling area. Finally, a case study which has 265 continuous variables, 68 binary variables and 318 constraints is effectively solved in LINGO 8.0 with the proposed approaches.