IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v219y2014i1p141-16710.1007-s10479-011-1036-7.html
   My bibliography  Save this article

An improved decomposition-based heuristic to design a water distribution network for an irrigation system

Author

Listed:
  • Graça Gonçalves
  • Luís Gouveia
  • Margarida Pato

Abstract

In this paper the authors address a pressurized water distribution network design problem for irrigation purposes. Two mixed binary nonlinear programming models are proposed for this NP-hard problem. Furthermore, a heuristic algorithm is presented for the problem, which considers a decomposition sequential scheme, based on linearization of the second model, coupled with constructive and local search procedures designed to achieve improved feasible solutions. To evaluate the robustness of the method we tested it on several instances generated from a real application. The best solutions obtained are finally compared with solutions provided by standard software. These computational experiments enable the authors to conclude that the decomposition sequential heuristic is a good approach to this difficult real problem. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • Graça Gonçalves & Luís Gouveia & Margarida Pato, 2014. "An improved decomposition-based heuristic to design a water distribution network for an irrigation system," Annals of Operations Research, Springer, vol. 219(1), pages 141-167, August.
    • Handle: RePEc:spr:annopr:v:219:y:2014:i:1:p:141-167:10.1007/s10479-011-1036-7
    DOI: 10.1007/s10479-011-1036-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-011-1036-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-011-1036-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Faiz A. Al-Khayyal & James E. Falk, 1983. "Jointly Constrained Biconvex Programming," Mathematics of Operations Research, INFORMS, vol. 8(2), pages 273-286, May.
    2. Graça Gonçalves & Margarida Vaz Pato, 2000. "A three‐phase procedure for designing an irrigation system's water distribution network," Annals of Operations Research, Springer, vol. 94(1), pages 163-179, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Bohong Wang & Yongtu Liang & Wei Zhao & Yun Shen & Meng Yuan & Zhimin Li & Jian Guo, 2021. "A Continuous Pump Location Optimization Method for Water Pipe Network Design," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 447-464, January.
    2. Shiono, Naoshi & Suzuki, Hisatoshi & Saruwatari, Yasufumi, 2019. "A dynamic programming approach for the pipe network layout problem," European Journal of Operational Research, Elsevier, vol. 277(1), pages 52-61.
    3. Sushil Gupta & Hossein Rikhtehgar Berenji & Manish Shukla & Nagesh N. Murthy, 2023. "Opportunities in farming research from an operations management perspective," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1577-1596, June.
    4. Baozhen Yao & Bin Yu & Ping Hu & Junjie Gao & Mingheng Zhang, 2016. "An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot," Annals of Operations Research, Springer, vol. 242(2), pages 303-320, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. N. V. Thoai, 2000. "Duality Bound Method for the General Quadratic Programming Problem with Quadratic Constraints," Journal of Optimization Theory and Applications, Springer, vol. 107(2), pages 331-354, November.
    2. Evrim Dalkiran & Hanif Sherali, 2013. "Theoretical filtering of RLT bound-factor constraints for solving polynomial programming problems to global optimality," Journal of Global Optimization, Springer, vol. 57(4), pages 1147-1172, December.
    3. Gabriele Eichfelder & Peter Kirst & Laura Meng & Oliver Stein, 2021. "A general branch-and-bound framework for continuous global multiobjective optimization," Journal of Global Optimization, Springer, vol. 80(1), pages 195-227, May.
    4. Sonia Cafieri & Jon Lee & Leo Liberti, 2010. "On convex relaxations of quadrilinear terms," Journal of Global Optimization, Springer, vol. 47(4), pages 661-685, August.
    5. Keith Zorn & Nikolaos Sahinidis, 2014. "Global optimization of general nonconvex problems with intermediate polynomial substructures," Journal of Global Optimization, Springer, vol. 59(2), pages 673-693, July.
    6. Tsao, Yu-Chung & Lu, Jye-Chyi & An, Na & Al-Khayyal, Faiz & Lu, Richard W. & Han, Guanghua, 2014. "Retailer shelf-space management with trade allowance: A Stackelberg game between retailer and manufacturers," International Journal of Production Economics, Elsevier, vol. 148(C), pages 133-144.
    7. Al-Khayyal, Faiz & Hwang, Seung-June, 2007. "Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, Part I: Applications and model," European Journal of Operational Research, Elsevier, vol. 176(1), pages 106-130, January.
    8. Manuel Ruiz & Olivier Briant & Jean-Maurice Clochard & Bernard Penz, 2013. "Large-scale standard pooling problems with constrained pools and fixed demands," Journal of Global Optimization, Springer, vol. 56(3), pages 939-956, July.
    9. Marco Locatelli, 2016. "Polyhedral subdivisions and functional forms for the convex envelopes of bilinear, fractional and other bivariate functions over general polytopes," Journal of Global Optimization, Springer, vol. 66(4), pages 629-668, December.
    10. Hezhi Luo & Xiaodong Ding & Jiming Peng & Rujun Jiang & Duan Li, 2021. "Complexity Results and Effective Algorithms for Worst-Case Linear Optimization Under Uncertainties," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 180-197, January.
    11. Kristianto, Yohanes & Helo, Petri, 2014. "Product architecture modularity implications for operations economy of green supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 128-145.
    12. Juan P. Ruiz & Ignacio E. Grossmann, 2017. "Global optimization of non-convex generalized disjunctive programs: a review on reformulations and relaxation techniques," Journal of Global Optimization, Springer, vol. 67(1), pages 43-58, January.
    13. Wang, Yadong & Meng, Qiang & Du, Yuquan, 2015. "Liner container seasonal shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 141-161.
    14. Luathep, Paramet & Sumalee, Agachai & Lam, William H.K. & Li, Zhi-Chun & Lo, Hong K., 2011. "Global optimization method for mixed transportation network design problem: A mixed-integer linear programming approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(5), pages 808-827, June.
    15. E Bustamante-Cedeño & S Arora, 2008. "Stochastic and minimum regret formulations for transmission network expansion planning under uncertainties," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1547-1556, November.
    16. Kristianto, Yohanes & Helo, Petri, 2015. "Reprint of “Product architecture modularity implications for operations economy of green supply chains”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 74(C), pages 63-80.
    17. Marco Locatelli, 2014. "A technique to derive the analytical form of convex envelopes for some bivariate functions," Journal of Global Optimization, Springer, vol. 59(2), pages 477-501, July.
    18. Harold P. Benson, 2004. "Concave envelopes of monomial functions over rectangles," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 467-476, June.
    19. Marco Locatelli, 2016. "Non polyhedral convex envelopes for 1-convex functions," Journal of Global Optimization, Springer, vol. 65(4), pages 637-655, August.
    20. Harsha Nagarajan & Mowen Lu & Site Wang & Russell Bent & Kaarthik Sundar, 2019. "An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs," Journal of Global Optimization, Springer, vol. 74(4), pages 639-675, August.

    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:spr:annopr:v:219:y:2014:i:1:p:141-167:10.1007/s10479-011-1036-7. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.