JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.1 P.9-19

http://doi.org/10.1631/jzus.2005.A0009

Hybid heuristic and mathematical programming in oil pipelines networks: Use of immigrants

Author(s): La Cruz J.M. De¹, A. Herrn-Gonzlez¹, J.L. Risco-Martn², B. Andrs-Toro¹
Affiliation(s): 1. Department of Computer Architecture and Automatic Control, Complutense University of Madrid, 28040 Madrid, Spain; more
Corresponding email(s): aherrang@fis.ucm.es
Key Words: MOEA, MILP, Hybrid algorithm, Constraints

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DE LA CRUZ J.M., HERRÁN-GONZÁLEZ A., RISCO-MARTÍN J.L., ANDRÉS-TORO B.. Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants[J]. Journal of Zhejiang University Science A, 2005, 6(1): 9-19.

@article{title="Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants",
author="DE LA CRUZ J.M., HERRÁN-GONZÁLEZ A., RISCO-MARTÍN J.L., ANDRÉS-TORO B.",
journal="Journal of Zhejiang University Science A",
volume="6",
number="1",
pages="9-19",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0009"
}

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%T Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0009

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A1 - DE LA CRUZ J.M.
A1 - HERRÁ
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A1 - RISCO-MARTÍ
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0009

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: We solve the problem of petroleum products distribution through oil pipelines networks. This problem is modelled and solved using two techniques: A heuristic method like a multiobjective evolutionary algorithm and Mathematical Programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a repairing function to avoid infeasible solutions. In the Mathematical Programming approach the multiobjective optimization is solved using the Constraint Method in Mixed Integer Linear Programming. Some constraints of the mathematical model are nonlinear, so they are linearized. The results obtained with both methods for one concrete network are presented. They are compared with a hybrid solution, where we use the results obtained by Mathematical Programming as the seed of the evolutionary algorithm.

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References

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[5] De la Cruz, J.M., Risco-Martn, J.L., Herrn-Gonzlez, A., 2004. Hybrid Heuristic and Mathematical Programming in Oil Pipelines Networks. , IEEE Congress on Evolutionary Computation, 1479-1486. :1479-1486.

[6] Luenberger, D.G., 1984. Linear and Nonlinear Programming, 2nd Ed, Reading. Addison-Wesley, New York,:

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[9] Michalewicz, Z., 1995. Genetic Algorithms, Numerical Optimization, and Constraints. , Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, San Francisco, CA, 151-158. :151-158.

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