IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v22y2022i5d10.1007_s12351-022-00738-6.html
   My bibliography  Save this article

A novel two-phase decomposition-based algorithm to solve MINLP pipeline scheduling problem

Author

Listed:
  • Neda Beheshti Asl

    (Amirkabir University of Technology)

  • S. A. MirHassani

    (Amirkabir University of Technology)

  • S. Relvas

    (Universidade de Lisboa)

  • F. Hooshmand

    (Amirkabir University of Technology)

Abstract

Decomposition-based algorithms have been successfully applied in the literature to solve NP-hard optimization problems. This paper presents an efficient decomposition-based heuristic to solve a new variant of the pipeline scheduling problem in which, besides minimizing the interface and demand shortage, the flow-rate stability of batches is also taken into account. Flow-rate stability has a great impact on the reduction of the energy consumed by pumping, and to the best of our knowledge, it has not been addressed in the continuous-time models of the pipeline scheduling problem. Thus, from the modeling perspective, a new continuous-time mixed-integer nonlinear programming (MINLP) model is developed, and from the solution viewpoint, nonlinear terms are remedied by a decomposition technique. Computational results over real-world case studies and randomly generated instances confirm that the proposed method is able to generate near-optimal solutions within a short amount of time; further, they show that the proposed model can result in more stable flow-rates compared to existing models.

Suggested Citation

  • Neda Beheshti Asl & S. A. MirHassani & S. Relvas & F. Hooshmand, 2022. "A novel two-phase decomposition-based algorithm to solve MINLP pipeline scheduling problem," Operational Research, Springer, vol. 22(5), pages 4829-4863, November.
  • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00738-6
    DOI: 10.1007/s12351-022-00738-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-022-00738-6
    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/s12351-022-00738-6?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. Fischetti, Matteo & Monaci, Michele, 2020. "A branch-and-cut algorithm for Mixed-Integer Bilinear Programming," European Journal of Operational Research, Elsevier, vol. 282(2), pages 506-514.
    2. Kirschstein, Thomas, 2018. "Planning of multi-product pipelines by economic lot scheduling models," European Journal of Operational Research, Elsevier, vol. 264(1), pages 327-339.
    3. R. Francis & T. Lowe & M. Rayco & A. Tamir, 2009. "Aggregation error for location models: survey and analysis," Annals of Operations Research, Springer, vol. 167(1), pages 171-208, March.
    4. Zhang, Haoran & Liang, Yongtu & Liao, Qi & Wu, Mengyu & Yan, Xiaohan, 2017. "A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations," Energy, Elsevier, vol. 119(C), pages 612-628.
    5. Mostafaei, Hossein & Castro, Pedro M. & Oliveira, Fabricio & Harjunkoski, Iiro, 2021. "Efficient formulation for transportation scheduling of single refinery multiproduct pipelines," European Journal of Operational Research, Elsevier, vol. 293(2), pages 731-747.
    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. Mostafaei, Hossein & Castro, Pedro M. & Oliveira, Fabricio & Harjunkoski, Iiro, 2021. "Efficient formulation for transportation scheduling of single refinery multiproduct pipelines," European Journal of Operational Research, Elsevier, vol. 293(2), pages 731-747.
    2. Mostafaei, Hossein & Castro, Pedro M. & Relvas, Susana & Harjunkoski, Iiro, 2021. "A holistic MILP model for scheduling and inventory management of a multiproduct oil distribution system," Omega, Elsevier, vol. 98(C).
    3. Chandra Ade Irawan & Dylan Jones, 2019. "Formulation and solution of a two-stage capacitated facility location problem with multilevel capacities," Annals of Operations Research, Springer, vol. 272(1), pages 41-67, January.
    4. Chandra Irawan & Said Salhi, 2015. "Solving large $$p$$ p -median problems by a multistage hybrid approach using demand points aggregation and variable neighbourhood search," Journal of Global Optimization, Springer, vol. 63(3), pages 537-554, November.
    5. Jing Yao & Alan T. Murray, 2014. "Serving regional demand in facility location," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 643-662, August.
    6. Long, Yin & Yoshida, Yoshikuni & Fang, Kai & Zhang, Haoran & Dhondt, Maya, 2019. "City-level household carbon footprint from purchaser point of view by a modified input-output model," Applied Energy, Elsevier, vol. 236(C), pages 379-387.
    7. Escudero, Laureano F. & Monge, Juan F. & Rodríguez-Chía, Antonio M., 2020. "On pricing-based equilibrium for network expansion planning. A multi-period bilevel approach under uncertainty," European Journal of Operational Research, Elsevier, vol. 287(1), pages 262-279.
    8. Vidovic, Milorad & Dimitrijevic, Branka & Ratkovic, Branislava & Simic, Vladimir, 2011. "A novel covering approach to positioning ELV collection points," Resources, Conservation & Recycling, Elsevier, vol. 57(C), pages 1-9.
    9. Mehrnoosh Taherkhani, 2020. "An MILP approach for scheduling of tree-like pipelines with dual purpose terminals," Operational Research, Springer, vol. 20(4), pages 2133-2161, December.
    10. Richard Francis & Timothy Lowe, 2014. "Comparative error bound theory for three location models: continuous demand versus discrete demand," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 144-169, April.
    11. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    12. Alexandris, George & Giannikos, Ioannis, 2010. "A new model for maximal coverage exploiting GIS capabilities," European Journal of Operational Research, Elsevier, vol. 202(2), pages 328-338, April.
    13. Irawan, Chandra Ade & Salhi, Said & Scaparra, Maria Paola, 2014. "An adaptive multiphase approach for large unconditional and conditional p-median problems," European Journal of Operational Research, Elsevier, vol. 237(2), pages 590-605.
    14. Jia, Tao & Carling, Kenneth & Håkansson, Johan, 2013. "Trips and their CO2 emissions to and from a shopping center," Journal of Transport Geography, Elsevier, vol. 33(C), pages 135-145.
    15. Schmid, Verena & Doerner, Karl F., 2010. "Ambulance location and relocation problems with time-dependent travel times," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1293-1303, December.
    16. Andreas Lundell & Jan Kronqvist, 2022. "Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT," Journal of Global Optimization, Springer, vol. 82(4), pages 863-896, April.
    17. Shanbi Peng & Zhe Zhang & Yongqiang Ji & Laimin Shi, 2022. "Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm," Energies, MDPI, vol. 15(20), pages 1-19, October.
    18. Babaee, Sara & Araghi, Mojtaba & Rostami, Borzou, 2022. "Coordinating transportation and pricing policies for perishable products," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 105-125.
    19. Alan Murray, 2010. "Advances in location modeling: GIS linkages and contributions," Journal of Geographical Systems, Springer, vol. 12(3), pages 335-354, September.
    20. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).

    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:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00738-6. 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.