MURAL - Maynooth University Research Archive Library



    A method for member selection of cross-functional teams using the individual and collaborative performances


    Feng, Bo and Jiang, Zhong-Zhong and Fan, Zhi-Ping and Fu, Na (2010) A method for member selection of cross-functional teams using the individual and collaborative performances. European Journal of Operational Research, 203. pp. 652-661. ISSN 0377-2217

    [img]
    Preview
    Download (1MB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    The member selection problem is an important aspect of the formation of cross-functional teams (CFTs). Selecting suitable members from a set of candidates will facilitate the successful task accomplishment. In the existing studies of member selection, the individual performance concerning a single candidate is mostly used, whereas the collaborative performance associating with a pair of candidates is overlooked. In this paper, as a solution to this problem, we propose a method for member selection of CFTs, where both the individual performance of candidates and the collaborative performance between candidates are considered. In order to select the desired members, firstly, a multi-objective 0–1 programming model is built using the individual and collaborative performances, which is an NP-hard problem. To solve the model, we develop an improved nondominated sorting genetic algorithm II (INSGA-II). Furthermore, a real example is employed to illustrate the suitability of the proposed method. Additionally, extensive computational experiments to compare INSGA-II with the nondominated sorting genetic algorithm II (NSGA-II) are conducted and much better performance of INSGA-II is observed.

    Item Type: Article
    Additional Information: The definitive version of this article is available at doi:10.1016/j.ejor.2009.08.017 © 2009 Elsevier B.V
    Keywords: Cross-functional team; Member selection; Individual and collaborative performances; Multi-objective 0–1 programming; Nondominated sorting genetic algorithm II;
    Academic Unit: Faculty of Social Sciences
    Item ID: 5697
    Identification Number: https://doi.org/10.1016/j.ejor.2009.08.017
    Depositing User: Na Fu
    Date Deposited: 18 Apr 2016 15:56
    Journal or Publication Title: European Journal of Operational Research
    Publisher: Elsevier
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year