Project risk management of the construction industry enterprises based on fuzzy set theory

  • Received September 25, 2019;
    Accepted November 30, 2019;
    Published December 11, 2019
  • Author(s)
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  • Article Info
    Volume 17 2019, Issue #4, pp. 203-213
  • Cited by
    3 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

The construction industry is a crucially important element of the Ukrainian economy, since its development and performance affect other industries. The economic recession consequences and the unforeseen recent events, caused by different types of risks, have adversely affected the construction industry development and necessitated the search for modern methods of risk management. The study is based on a sample of five projects from five construction industry enterprises and covered the period of 2010–2018. A set of project risks, investigated by the group of experts, was analyzed based on fuzzy set theory, and included seven phases of the fuzzy set model construction to assess project risks of construction industry enterprises. Based on the identified elements of a fuzzy set model and a set of significant project risks, a value classifier of significant project risks for construction industry enterprises was developed. This allowed to estimate the current values of project risk indicators and to identify them by levels of their fuzzy subset membership. Besides, a classifier for the quantitative assessment of the total project risks level for investment projects was developed, which allowed estimating the value of the aggregate indicator. In order to improve the existed methodology, the study suggested introducing probabilistic values for the risk of project failure depending on the significance of the overall project risks. Accordingly, the paper identifies the probability of significant project risks simultaneous occurring during the project implementation. However, the higher the likelihood of risk, the higher the probability of investment project failure.

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    • Figure 1. The system of trapezoidal membership functions Fі(x) on the 01 carrier
    • Table 1. Indicators of the construction companies’ significant project risks according to the reduced impact degree on investment projects
    • Table 2. The classifier of indicator values for significant project risks Xij of construction industry enterprises according to a single quantification metric
    • Table 3. The classifier of the aggregate project risk quantification assessment for investment projects at the construction industry enterprises
    • Table 4. A quantification system for assessing the level of threat of the investment project failure of construction industry enterprises
    • Table 5. The membership level matrix of indicator carriers of construction enterprises’ significant project risks in fuzzy subsets of linguistic variable values of the project risk {RPRі} term-set
    • Table 6. Quantification estimate results for the level of integrated project risks for Kyivmiskbud-1’s investment projects