IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/101376.html
   My bibliography  Save this article

A Review of Piecewise Linearization Methods

Author

Listed:
  • Ming-Hua Lin
  • John Gunnar Carlsson
  • Dongdong Ge
  • Jianming Shi
  • Jung-Fa Tsai

Abstract

Various optimization problems in engineering and management are formulated as nonlinear programming problems. Because of the nonconvexity nature of this kind of problems, no efficient approach is available to derive the global optimum of the problems. How to locate a global optimal solution of a nonlinear programming problem is an important issue in optimization theory. In the last few decades, piecewise linearization methods have been widely applied to convert a nonlinear programming problem into a linear programming problem or a mixed-integer convex programming problem for obtaining an approximated global optimal solution. In the transformation process, extra binary variables, continuous variables, and constraints are introduced to reformulate the original problem. These extra variables and constraints mainly determine the solution efficiency of the converted problem. This study therefore provides a review of piecewise linearization methods and analyzes the computational efficiency of various piecewise linearization methods.

Suggested Citation

  • Ming-Hua Lin & John Gunnar Carlsson & Dongdong Ge & Jianming Shi & Jung-Fa Tsai, 2013. "A Review of Piecewise Linearization Methods," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, November.
    • Handle: RePEc:hin:jnlmpe:101376
    DOI: 10.1155/2013/101376
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2013/101376.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2013/101376.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/101376?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Guanyu & Dong, Qianyu & Zhao, Yuming & Wang, Han & Jian, Linni & Jia, Youwei, 2023. "Bilevel optimization approach to fast charging station planning in electrified transportation networks," Applied Energy, Elsevier, vol. 350(C).
    2. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
    3. Lars Wietschel & Lukas Messmann & Andrea Thorenz & Axel Tuma, 2021. "Environmental benefits of large‐scale second‐generation bioethanol production in the EU: An integrated supply chain network optimization and life cycle assessment approach," Journal of Industrial Ecology, Yale University, vol. 25(3), pages 677-692, June.
    4. Pantelis Broukos & Antonios Fragkogios & Nilay Shah, 2022. "A Linearized Mathematical Formulation for Combined Centralized and Distributed Waste Water Treatment Network Design," SN Operations Research Forum, Springer, vol. 3(3), pages 1-29, September.
    5. Manuel Parraga & José Vuelvas & Benjamín González-Díaz & Leonardo Rodríguez-Urrego & Arturo Fajardo, 2024. "A Systematic Review of Isolated Water and Energy Microgrids: Infrastructure, Optimization of Management Strategies, and Future Trends," Energies, MDPI, vol. 17(12), pages 1-28, June.
    6. Galvan-Cara, Aldwin-Lois & Graells, Moisès & Espuña, Antonio, 2022. "Application of Industrial Symbiosis principles to the management of utility networks," Applied Energy, Elsevier, vol. 305(C).
    7. Manfren, Massimiliano & James, Patrick AB. & Tronchin, Lamberto, 2022. "Data-driven building energy modelling – An analysis of the potential for generalisation through interpretable machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Ahmadisedigh, Hossein & Gosselin, Louis, 2022. "Combined heating and cooling networks with part-load efficiency curves: Optimization based on energy hub concept," Applied Energy, Elsevier, vol. 307(C).
    9. Bitencourt, Leonardo & Dias, Bruno & Soares, Tiago & Borba, Bruno & Quirós-Tortós, Jairo, 2023. "e-Carsharing siting and sizing DLMP-based under demand uncertainty," Applied Energy, Elsevier, vol. 330(PB).
    10. Nebuloni, Riccardo & Meraldi, Lorenzo & Bovo, Cristian & Ilea, Valentin & Berizzi, Alberto & Sinha, Snigdh & Tamirisakandala, Raviteja Bharadwaj & Raboni, Pietro, 2023. "A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model," Applied Energy, Elsevier, vol. 334(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:101376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.