IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v27y2021i5d10.1007_s10732-021-09481-1.html
   My bibliography  Save this article

An adaptive large neighbourhood search algorithm for diameter bounded network design problems

Author

Listed:
  • Michele Garraffa

    (University College Cork)

  • Deepak Mehta

    (University College Cork)

  • Barry O’Sullivan

    (University College Cork)

  • Cemalettin Ozturk

    (Munster Technological University, Process, Energy and Transport Engineering)

  • Luis Quesada

    (University College Cork)

Abstract

This paper focuses on designing a diameter - constrained network where the maximum distance between any pair of nodes is bounded. The objective considered is to minimise a weighted sum of the total length of the links followed by the total length of the paths between the pairs of nodes. First, the problem is formulated in terms of Mixed Integer Linear Programming and Constraint Programming to provide two alternative exact approaches. Then, an adaptive large neighbourhood search (LNS) to overcome memory and runtime limitations of the exact methods in large size instances is proposed. Such approach is based on computing an initial solution and repeatedly improve it by solving relatively small subproblems. We investigate various alternatives for finding an initial solution and propose two different heuristics for selecting subproblems. We have introduced a tighter lower bound, which demonstrates the quality of the solution obtained by the proposed approach. The performance of the proposed approach is assessed using three real-world network topologies from Ireland, UK and Italy, which are taken from national telecommunication operators and are used to design a transparent optical core network. Our results demonstrate that the LNS approach is scalable to large networks and it can compute very high quality solutions that are close to being optimal.

Suggested Citation

  • Michele Garraffa & Deepak Mehta & Barry O’Sullivan & Cemalettin Ozturk & Luis Quesada, 2021. "An adaptive large neighbourhood search algorithm for diameter bounded network design problems," Journal of Heuristics, Springer, vol. 27(5), pages 887-922, October.
    • Handle: RePEc:spr:joheur:v:27:y:2021:i:5:d:10.1007_s10732-021-09481-1
    DOI: 10.1007/s10732-021-09481-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-021-09481-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10732-021-09481-1?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. David Pisinger & Stefan Ropke, 2010. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 399-419, Springer.
    Full references (including those not matched with items on IDEAS)

    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. El Mehdi, Er Raqabi & Ilyas, Himmich & Nizar, El Hachemi & Issmaïl, El Hallaoui & François, Soumis, 2023. "Incremental LNS framework for integrated production, inventory, and vessel scheduling: Application to a global supply chain," Omega, Elsevier, vol. 116(C).
    2. Bach, Lukas & Hasle, Geir & Schulz, Christian, 2019. "Adaptive Large Neighborhood Search on the Graphics Processing Unit," European Journal of Operational Research, Elsevier, vol. 275(1), pages 53-66.
    3. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    4. Guerrero, W.J. & Prodhon, C. & Velasco, N. & Amaya, C.A., 2013. "Hybrid heuristic for the inventory location-routing problem with deterministic demand," International Journal of Production Economics, Elsevier, vol. 146(1), pages 359-370.
    5. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    6. Andrea Lodi & Enrico Malaguti & Nicolás E. Stier-Moses & Tommaso Bonino, 2016. "Design and Control of Public-Service Contracts and an Application to Public Transportation Systems," Management Science, INFORMS, vol. 62(4), pages 1165-1187, April.
    7. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    8. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    9. Masson, Renaud & Lahrichi, Nadia & Rousseau, Louis-Martin, 2016. "A two-stage solution method for the annual dairy transportation problem," European Journal of Operational Research, Elsevier, vol. 251(1), pages 36-43.
    10. Timo Hintsch, 2019. "Large Multiple Neighborhood Search for the Soft-Clustered Vehicle-Routing Problem," Working Papers 1904, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    11. Ulrike Ritzinger & Jakob Puchinger & Richard Hartl, 2016. "Dynamic programming based metaheuristics for the dial-a-ride problem," Annals of Operations Research, Springer, vol. 236(2), pages 341-358, January.
    12. Peng Xu & Qixing Liu & Yuhu Wu, 2023. "Energy Saving-Oriented Multi-Depot Vehicle Routing Problem with Time Windows in Disaster Relief," Energies, MDPI, vol. 16(4), pages 1-15, February.
    13. Hatzenbühler, Jonas & Jenelius, Erik & Gidófalvi, Gyözö & Cats, Oded, 2023. "Modular vehicle routing for combined passenger and freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    14. Gläser, Sina, 2022. "A waste collection problem with service type option," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1216-1230.
    15. Timo Gschwind & Michael Drexl, 2016. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Working Papers 1624, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    16. Ruf, Moritz & Cordeau, Jean-François, 2021. "Adaptive large neighborhood search for integrated planning in railroad classification yards," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 26-51.
    17. Majid Eskandarpour & Pierre Dejax & Olivier Péton, 2019. "Multi-Directional Local Search for Sustainable Supply Chain Network Design," Post-Print hal-02407741, HAL.
    18. Aksen, Deniz & Kaya, Onur & Sibel Salman, F. & Tüncel, Özge, 2014. "An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem," European Journal of Operational Research, Elsevier, vol. 239(2), pages 413-426.
    19. Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2020. "Integrating first-mile pickup and last-mile delivery on shared vehicle routes for efficient urban e-commerce distribution," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 26-62.
    20. Daniela Guericke & Leena Suhl, 2017. "The home health care problem with working regulations," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 977-1010, October.

    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:joheur:v:27:y:2021:i:5:d:10.1007_s10732-021-09481-1. 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.