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Construction projects planning using network model with the fuzzy decision node

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In the construction practice, at the stage of planning of a construction project, planners are trying to take into account the possibility of unfavorable situations and their consequences during the… Click to show full abstract

In the construction practice, at the stage of planning of a construction project, planners are trying to take into account the possibility of unfavorable situations and their consequences during the project execution. Therefore, planning decisions should use appropriate tools for uncertainty modeling as well as consider alternative options for the implementation of the entire undertaking or the most sensitive (critical) works. Taking into account the fact that the management and planning of construction projects is carried out in conditions of non-stochastic uncertainty, the authors propose to create an alternative model with a fuzzy decision node based on classical network models. This approach allows to model activities that are alternative to each other. An important methodological element is the fuzzy assessment of the considered alternatives with the use of fuzzy set theory. The proposed alternative network model with a fuzzy decision node gives the possibility to conduct a comprehensive analysis with the possibility of taking into account and modeling the uncertainty of input data in an easy way. The numerical example shows the possibility of quick selection of the best variant based on the adopted assessment criteria and allows to serialize work variants, which gives additional information in the alternative activities of the network model in the form of preferences. The combination of a decision model in the form of a decision node with a network model can be useful in both strategic and tactical planning of the implementation of construction projects.

Keywords: construction; model; decision node; network model

Journal Title: International Journal of Environmental Science and Technology
Year Published: 2019

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