This paper aims to develop a model for selecting project team members. In this model, while knowledge sharing among individuals is maximized, the project costs and the workload balance among employees are also optimized.
The problem of project team formation is formulated as a fuzzy multi-objective 0-1 integer programming model. Afterward, to deal with uncertainty in the decision-making on the candidates’ abilities and the project requirements, the fuzzy multi-objective chance-constrained programming approach is adopted. Finally, by combining the non-dominated sorting genetic algorithm II and the fuzzy simulation algorithms, a method is proposed to solve the problem.
The computational results of the proposed model in a case study of project team formation in a large Iranian company from the shipbuilding industry evidently demonstrated its effectiveness in providing Pareto-optimal solutions for the team composition.
Seemingly for the first time, this paper develops a model to optimize knowledge sharing and improve the project efficiency through the selection of appropriate project team members.
Hosseini, S.M. and Akhavan, P. (2017), "A model for project team formation in complex engineering projects under uncertainty: A knowledge-sharing approach", Kybernetes, Vol. 46 No. 7, pp. 1131-1157. https://doi.org/10.1108/K-06-2015-0150
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