IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v19y2001i2p207-215.html
   My bibliography  Save this article

Genetic algorithm model in optimizing the use of labour

Author

Listed:
  • C. M. Tam
  • Thomas Tong
  • S. O. Cheung
  • Albert Chan

Abstract

The construction industry is characterized by the existence of multiple trades and crafts. With the existence of multiple-tiers of labour-only-subcontracting in Hong Kong, tradesmen are normally assigned to tasks of a narrowly defined skill. Lately, there has been a call for the adoption of a directly employed labour scheme by the Hong Kong Housing Authority and the public works departments in Hong Kong in order to improve both safety and quality. However, current industry practice has hindered the adoption of directly employed labour, which requires assigning tradesmen to broadly defined task groupings. In implementing the scheme, the first thing to be resolved is how to maximize the levels of use of workers, because that is the major financial incentive to encourage contractors to adopt the directly employed labour policy. This coupled with the shortage of some skilled craft workers call into question the traditionally accepted 'single-skilled' or 'single-task' approach in labour deployment. A genetic algorithm model is developed to optimize the labour deployment and practical examples are presented. The optimization results are very promising, confirming the practical application value of the model.

Suggested Citation

  • C. M. Tam & Thomas Tong & S. O. Cheung & Albert Chan, 2001. "Genetic algorithm model in optimizing the use of labour," Construction Management and Economics, Taylor & Francis Journals, vol. 19(2), pages 207-215.
    • Handle: RePEc:taf:conmgt:v:19:y:2001:i:2:p:207-215
    DOI: 10.1080/01446190150505126
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/01446190150505126
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01446190150505126?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. Sou-Sen Leu & Chung-Huei Yang, 1999. "A genetic-algorithm-based resource-constrained construction scheduling system," Construction Management and Economics, Taylor & Francis Journals, vol. 17(6), pages 767-776.
    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. Hong Zhang & Heng Li & C. M. Tam, 2006. "Heuristic scheduling of resource-constrained, multiple-mode and repetitive projects," Construction Management and Economics, Taylor & Francis Journals, vol. 24(2), pages 159-169.
    2. Wei He & Wenjing Li & Wei Wang, 2021. "Developing a Resource Allocation Approach for Resource-Constrained Construction Operation under Multi-Objective Operation," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    3. Omid Bozorg Haddad & Mahsa Mirmomeni & Mahboubeh Zarezadeh Mehrizi & Miguel Mariño, 2010. "Finding the shortest path with honey-bee mating optimization algorithm in project management problems with constrained/unconstrained resources," Computational Optimization and Applications, Springer, vol. 47(1), pages 97-128, September.
    4. H. Zhang & C. M. Tam, 2003. "Fuzzy decision-making for dynamic resource allocation," Construction Management and Economics, Taylor & Francis Journals, vol. 21(1), pages 31-41.
    5. Sou-Sen Leu & Theresia Daisy Nattali Suparman & Cathy Chang-Wei Hung, 2021. "An Exploratory Study on Optimal Iterative Design Schedules with the Consideration of Design Quality and Resource Constraints," Sustainability, MDPI, vol. 13(8), pages 1-14, April.

    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:taf:conmgt:v:19:y:2001:i:2:p:207-215. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RCME20 .

    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.