IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v36y2018i3d10.1007_s10878-018-0253-2.html
   My bibliography  Save this article

Planning personnel retraining: column generation heuristics

Author

Listed:
  • Oliver G. Czibula

    (University of Technology Sydney)

  • Hanyu Gu

    (University of Technology Sydney)

  • Yakov Zinder

    (University of Technology Sydney)

Abstract

Retraining of staff is a compulsory managerial function in many organisations and often requires planning for a large number of employees. The large scale of this problem and various restrictions on the resultant assignment to classes make this planning challenging. The paper presents a complexity analysis of this problem together with linear and nonlinear mathematical programming formulations. Three different column generation based optimisation procedures and a large neighbourhood search procedure, incorporating column generation, are compared by means of computational experiments. The experiments used data typical to large electricity distributors.

Suggested Citation

  • Oliver G. Czibula & Hanyu Gu & Yakov Zinder, 2018. "Planning personnel retraining: column generation heuristics," Journal of Combinatorial Optimization, Springer, vol. 36(3), pages 896-915, October.
    • Handle: RePEc:spr:jcomop:v:36:y:2018:i:3:d:10.1007_s10878-018-0253-2
    DOI: 10.1007/s10878-018-0253-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-018-0253-2
    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/s10878-018-0253-2?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.
    2. García-Martínez, C. & Rodriguez, F.J. & Lozano, M., 2014. "Tabu-enhanced iterated greedy algorithm: A case study in the quadratic multiple knapsack problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 454-463.
    3. Alberto Caprara & David Pisinger & Paolo Toth, 1999. "Exact Solution of the Quadratic Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 11(2), pages 125-137, May.
    4. Yuning Chen & Jin-Kao Hao, 2015. "Iterated responsive threshold search for the quadratic multiple knapsack problem," Annals of Operations Research, Springer, vol. 226(1), pages 101-131, March.
    5. Bolte, Andreas & Thonemann, Ulrich Wilhelm, 1996. "Optimizing simulated annealing schedules with genetic programming," European Journal of Operational Research, Elsevier, vol. 92(2), pages 402-416, July.
    6. Zvi Drezner, 2003. "A New Genetic Algorithm for the Quadratic Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 320-330, August.
    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. Yuning Chen & Jin-Kao Hao, 2015. "Iterated responsive threshold search for the quadratic multiple knapsack problem," Annals of Operations Research, Springer, vol. 226(1), pages 101-131, March.
    2. Zheng Wang & Wei Xu & Xiangpei Hu & Yong Wang, 2022. "Inventory allocation to robotic mobile-rack and picker-to-part warehouses at minimum order-splitting and replenishment costs," Annals of Operations Research, Springer, vol. 316(1), pages 467-491, September.
    3. Loiola, Eliane Maria & de Abreu, Nair Maria Maia & Boaventura-Netto, Paulo Oswaldo & Hahn, Peter & Querido, Tania, 2007. "A survey for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 657-690, January.
    4. David Bergman, 2019. "An Exact Algorithm for the Quadratic Multiknapsack Problem with an Application to Event Seating," INFORMS Journal on Computing, INFORMS, vol. 31(3), pages 477-492, July.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. Pawel Kalczynski & Jack Brimberg & Zvi Drezner, 2022. "Less is more: discrete starting solutions in the planar p-median problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 34-59, April.
    11. Jean-François Cordeau & Manlio Gaudioso & Gilbert Laporte & Luigi Moccia, 2006. "A Memetic Heuristic for the Generalized Quadratic Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 18(4), pages 433-443, November.
    12. 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.
    13. Fabio Furini & Emiliano Traversi, 2019. "Theoretical and computational study of several linearisation techniques for binary quadratic problems," Annals of Operations Research, Springer, vol. 279(1), pages 387-411, August.
    14. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    15. 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.
    16. 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.
    17. 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.
    18. Caprara, Alberto, 2008. "Constrained 0-1 quadratic programming: Basic approaches and extensions," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1494-1503, June.
    19. 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.
    20. 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.

    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:jcomop:v:36:y:2018:i:3:d:10.1007_s10878-018-0253-2. 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.