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CLC number: TU375

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Received: 2004-05-08

Revision Accepted: 2004-10-11

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Cited: 4

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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


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.

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journal="Journal of Zhejiang University Science A",
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

References

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[2] Botros, K., Sennhauser, D., Jugowski, K., 2004. Multi-objective Optimization in Large Pipeline Networks using Genetic Algorithms. , Intenational Pipeline Conference, Calgary, Alberta, Canada, :

[3] Chankong, V., Haimes, Y.Y., 1983. Multiobjective Decision Making Theory and Methodology. , North Holland Series in System Science and Engineering, New York, :

[4] De la Cruz, J.M., Andrs-Toro, B., Herrn-Gonzlez, A., 2003. Multiobjective optimization of the transport in oil pipeline networks. , IEEE International Conference on Emerging Technologies and Factory Automation, 566-573. :566-573. 

[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,:

[7] Marglin, S.A., 1967. Public Investment Criteria, MIT Press, Cambridge, MA,:

[8] Marriott, K., Stuckey, P.J., 1998. Programming with Constraints: An Introduction, MIT Press, Cambridge, MA,:

[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. 

[10] Schrijver, A., 1986. Theory of Linear and Integer Programming, Wiley, Chichester,:

[11] Van Hentenryck, P., 1999. The OPL Optimization Programming Language, MIT Press, Cambridge, Massachusetts,:


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