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Cassar, R and Martin, H (2016) How To Choose A Project Manager Under Uncertainty. In: Chan, P W and Neilson, C J (Eds.), Proceedings 32nd Annual ARCOM Conference, 5-7 September 2016, Manchester UK. Association of Researchers in Construction Management, 619–628.

  • Type: Conference Proceedings
  • Keywords: Project Manager, Selection, Human Resource, Cloud Theory, Decision-making
  • ISBN/ISSN: 978-0-9955463-0-1
  • URL: http://www.arcom.ac.uk/-docs/proceedings/d997698a7a8b1bc0e8ce7b0a59e713fb.pdf
  • Abstract:

    The success of modern construction projects is heavily dependent on its assigned resources, the most important of these being the project manager. Such a person is responsible for seeing the project to fruition. Selecting a project manager is therefore a highly critical decision which is complicated because the process of choosing does not depend on any sole attribute but an analysis of several different characteristics of a candidate. This multi-criteria approach which requires the assignment of weights to criteria and candidates introduces uncertainty derived from the subjective judgment of the decision maker(s). This uncertainty is amplified, particularly, when the candidate pool is large and the persons comprising the pool are similarly qualified or experienced.  While Fuzzy Set theory has been applied to discriminate among candidates by addressing uncertainties originating from judgements of decision makers, random uncertainties has been absent from the theoretical discourse. Cloud Theory, which is an amalgamation of Fuzzy Set Theory and Probability Theory, is used to account for both fuzzy and random uncertainties. A forward normal cloud MATLAB and Excel model which utilizes normal distribution to create bell-curved membership functions is used to hierarchically evaluate and select the most capable project manager for a construction project. This new approach capitalizes on randomness between the fuzzy partitions among the alternatives. Such a model aids human resource managers by simplifying decisions where uncertainty exists.  A case study conducted at a construction firm illustrating the hiring exercise details how the preferences of decision makers can be applied to the model.