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Metaheuristics for Natural Gas Pipeline Networks

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Handbook of Heuristics

Abstract

In this chapter an overview of metaheuristic algorithms that have been very successful in tackling a particular class of natural gas pipeline network optimization problems is presented. In particular, the problem of minimizing fuel consumption incurred by the compressor stations driving natural gas in pipeline networks is addressed. This problem has been studied from different angles over the past few years by virtue of its tremendous economical impact. First, a general mathematical framework for this class of problems is presented. After establishing the most relevant model properties and fundamental network topologies, which are key factors for choosing an appropriate solution technique, current state-of-the-art metaheuristics are presented for handling different versions of this problem. This work concludes by highlighting the most relevant and important challenges of this very exciting area of research in natural gas transportation networks.

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Acknowledgements

The research of the author was supported by the Mexican Council for Science and Technology, grant CONACyT CB-2011-01-166397. The author would also like to thank the editors for their helpful remarks and suggestions.

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Authors and Affiliations

  1. Graduate Program in Systems Engineering, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, Mexico

    Roger Z. Ríos-Mercado

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  1. Roger Z. Ríos-Mercado
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Correspondence to Roger Z. Ríos-Mercado .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Ríos-Mercado, R.Z. (2018). Metaheuristics for Natural Gas Pipeline Networks. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_51

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