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

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  • Feng, Bo
  • Jiang, Zhong-Zhong
  • Fan, Zhi-Ping
  • Fu, Na

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.

Suggested Citation

  • Feng, Bo & Jiang, Zhong-Zhong & Fan, Zhi-Ping & Fu, Na, 2010. "A method for member selection of cross-functional teams using the individual and collaborative performances," European Journal of Operational Research, Elsevier, vol. 203(3), pages 652-661, June.
    • Handle: RePEc:eee:ejores:v:203:y:2010:i:3:p:652-661
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    2. Feng, Bo & Fan, Zhi-Ping & Li, Yanzhi, 2011. "A decision method for supplier selection in multi-service outsourcing," International Journal of Production Economics, Elsevier, vol. 132(2), pages 240-250, August.
    3. Miroslav Kelemen & Volodymyr Polishchuk & Beáta Gavurová & Róbert Rozenberg & Juraj Bartok & Ladislav Gaál & Martin Gera & Martin Kelemen, 2021. "Model of Evaluation and Selection of Expert Group Members for Smart Cities, Green Transportation and Mobility: From Safe Times to Pandemic Times," Mathematics, MDPI, vol. 9(11), pages 1-21, June.
    4. Lozano, M. & Molina, D. & GarcI´a-MartI´nez, C., 2011. "Iterated greedy for the maximum diversity problem," European Journal of Operational Research, Elsevier, vol. 214(1), pages 31-38, October.
    5. Keunho Choi & Gunwoo Kim & Yongmoo Suh & Donghee Yoo, 2017. "Assignment of collaborators to multiple business problems using genetic algorithm," Information Systems and e-Business Management, Springer, vol. 15(4), pages 877-895, November.
    6. Wu, Qinghua & Hao, Jin-Kao, 2015. "A review on algorithms for maximum clique problems," European Journal of Operational Research, Elsevier, vol. 242(3), pages 693-709.
    7. Pingping Cao & Jin Zheng & Mingyang Li & Yu Fu, 2023. "A Model for the Assignment of Emergency Rescuers Considering Collaborative Information," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    8. Wu, Qinghua & Hao, Jin-Kao, 2013. "A hybrid metaheuristic method for the Maximum Diversity Problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 452-464.
    9. Keunho Choi & Gunwoo Kim & Yongmoo Suh & Donghee Yoo, 0. "Assignment of collaborators to multiple business problems using genetic algorithm," Information Systems and e-Business Management, Springer, vol. 0, pages 1-19.

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