IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v282y2020i2p729-740.html
   My bibliography  Save this article

Multiobjective evolutionary algorithms for strategic deployment of resources in operational units

Author

Listed:
  • Drake, John H.
  • Starkey, Andrew
  • Owusu, Gilbert
  • Burke, Edmund K.

Abstract

Large-scale infrastructure networks require frequent maintenance, often performed by a team of skilled engineers spread over a large area. The set of tasks allocated to an engineer can have a huge impact on overall efficiency, whether that be in terms of time taken to complete all tasks, staffing costs or environmental costs in terms of emissions. When required to efficiently allocate a set of geographically distributed tasks to a maintenance engineering workforce, one approach is to define working areas for which teams of engineers are responsible. Often a key obstacle to overcome when looking for solutions is ensuring a balance between multiple competing objectives. In this paper, we employ a number of multiobjective evolutionary algorithms to analyse a simulation model for a real-world workforce optimisation problem used by BT. We provide a detailed analysis of the class of problems to be solved, where the workforce and a set of service distribution points must be split into smaller working areas, referred to as operational units. As the choice of how many operational units to split a larger working area into is critical, some of the practical considerations that must be made when addressing such problems are highlighted. This research has allowed the planning team at BT to understand the unique complexities of the nature of the problems they face in different areas of the UK, particularly with respect to the choice of number of operational units, and has strengthened their ability to design operational units effectively.

Suggested Citation

  • Drake, John H. & Starkey, Andrew & Owusu, Gilbert & Burke, Edmund K., 2020. "Multiobjective evolutionary algorithms for strategic deployment of resources in operational units," European Journal of Operational Research, Elsevier, vol. 282(2), pages 729-740.
    • Handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:729-740
    DOI: 10.1016/j.ejor.2019.02.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719301134
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.02.002?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. Li, Wenwen & Özcan, Ender & John, Robert, 2017. "Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation," Renewable Energy, Elsevier, vol. 105(C), pages 473-482.
    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. Duro, João A. & Ozturk, Umud Esat & Oara, Daniel C. & Salomon, Shaul & Lygoe, Robert J. & Burke, Richard & Purshouse, Robin C., 2023. "Methods for constrained optimization of expensive mixed-integer multi-objective problems, with application to an internal combustion engine design problem," European Journal of Operational Research, Elsevier, vol. 307(1), pages 421-446.
    2. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    3. Haywood, Adam B. & Lunday, Brian J. & Robbins, Matthew J. & Pachter, Meir N., 2022. "The weighted intruder path covering problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 347-358.

    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. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    2. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    3. Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    4. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    5. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
    6. 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.
    7. Butterwick, Thomas & Kheiri, Ahmed & Lulli, Guglielmo & Gromicho, Joaquim & Kreeft, Jasper, 2023. "Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm," Renewable Energy, Elsevier, vol. 208(C), pages 1-16.
    8. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    9. Croonenbroeck, Carsten & Hennecke, David, 2021. "A comparison of optimizers in a unified standard for optimization on wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    10. Drake, John H. & Kheiri, Ahmed & Özcan, Ender & Burke, Edmund K., 2020. "Recent advances in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 405-428.
    11. Longlong Leng & Yanwei Zhao & Jingling Zhang & Chunmiao Zhang, 2019. "An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem," IJERPH, MDPI, vol. 16(11), pages 1-28, June.
    12. Kirchner-Bossi, Nicolas & Porté-Agel, Fernando, 2024. "Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm," Renewable Energy, Elsevier, vol. 220(C).
    13. Longlong Leng & Yanwei Zhao & Zheng Wang & Jingling Zhang & Wanliang Wang & Chunmiao Zhang, 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints," Sustainability, MDPI, vol. 11(6), pages 1-31, March.
    14. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    15. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    16. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    17. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    18. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    19. Shaaban, S. & Albatal, A. & Mohamed, M.H., 2018. "Optimization of H-Rotor Darrieus turbines' mutual interaction in staggered arrangements," Renewable Energy, Elsevier, vol. 125(C), pages 87-99.

    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:eee:ejores:v:282:y:2020:i:2:p:729-740. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.