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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/115462...
Part of book or chapter of book . 2005 . Peer-reviewed
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A Hybrid Meta-heuristic Approach for Natural Gas Pipeline Network Optimization

Authors: Conrado Borraz-Sánchez; Roger Z. Ríos-Mercado;

A Hybrid Meta-heuristic Approach for Natural Gas Pipeline Network Optimization

Abstract

In this paper we propose a hybrid heuristic solution procedure for fuel cost minimization on gas transmission systems with a cyclic network topology, that is, networks with at least one cycle containing two or more compressor station arcs. Our heuristic solution methodology is based on a two-stage iterative procedure. In a particular iteration, at a first stage, gas flow variables are fixed in each network arc and optimal pressure variables in each network node are found via non-sequential dynamic programming. At a second stage, pressure variables are fixed and a short-term memory Tabu Search procedure is used for guiding the search in the flow variable space. Empirical evidence supports the effectiviness of the proposed procedure outperforming the best existing approach to the best of our knowledge.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
Top 10%
Top 10%
Average
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