A global optimization approach for a class of MINLP problems with applications to crude oil scheduling problem

Volume 8, Issue 5, pp 695--709 http://dx.doi.org/10.22436/jnsa.008.05.22

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Authors

Qianqian Duan - Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China. Genke Yang - Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China. Guanglin Xu - College of Mathematics and Information, Shanghai Lixin University of Commerce, China. Xueyan Duan - School of Economics and Management, Shanghai Maritime University, China.

Abstract

A global optimization algorithm is proposed to solve the crude oil schedule problem. We first developed a lower and upper bounding model by using a multiparametric disaggregation method. Secondly, the lower and the upper bounding models combined with finite state method (FSM) are incorporated to solve the bilinear programing problem jointly. The advantage of using FSM is that we can generate promising substructure and partial solution. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the algorithm has better global performance than some existing algorithms. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms commercial solvers.

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Keywords

• MINLP
• finite state method
• hybrid optimization.

MSC

•  90C11
•  90C26
•  90C56

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