CLC number: TU375
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 4
Clicked: 6488
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"
}
%0 Journal Article
%T Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants
%A DE LA CRUZ J.M.
%A HERRÁ
%A N-GONZÁ
%A LEZ A.
%A RISCO-MARTÍ
%A N J.L.
%A ANDRÉ
%A S-TORO B.
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 1
%P 9-19
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0009
TY - JOUR
T1 - Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants
A1 - DE LA CRUZ J.M.
A1 - HERRÁ
A1 - N-GONZÁ
A1 - LEZ A.
A1 - RISCO-MARTÍ
A1 - N J.L.
A1 - ANDRÉ
A1 - S-TORO B.
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 1
SP - 9
EP - 19
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0009
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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