Deviation monitoring model of construction projects, case study: a contracting company

Articles in Press

Document Type : Article

Authors

1 Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

2 PhD Student in Quality and Productivity, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

10.24200/j65.2024.62035.2347

Abstract

Project management is always looking for ways to complete project on time, quality and cost according to the project contract .Due to the existence of various risks and increased uncertainty in business environments, a high percentage of the projects have deviations when compared with the base plan. The purpose of this research is to continously monitoring the deviation of the project by evaluating the deviation of cost, time and quality simultaneously under the conditions of uncertainty. By conducting a pairwise comparison between cost, time and quality factors and interviewing experts of a contractor company, the relative importance of these factors has been determined. The most important risks of the project have been identified by interviewing experts in the contractor company. The risk assessment has been carried out with the failure mode and effect analysis and fuzzy logic method. By using the approach of fuzzy inference system and Bayesian networks, project deviation is predicted. In the fuzzy inference system, project risks are considered as input variables in the form of triangle fuzzy number and project deviation is obtained as the output variable of the cohesive fuzzy inference system in Matlab software. In the Bayesian network approach, the initial and conditional probabilities of the nodes have been obtained by using the experts' opinion and the project deviation has been investigated using the network between risks in AgenaRisk software. To estimate the validity of the results of the models, the mean square error criterion was used. By comparing the actual deviation percentage of projects implemented in the mentioned company with the estimated deviation percentage of the models, the mean squared error in the fuzzy inference method is less compared to the Bayesian network method, and the fuzzy inference method with the mean squared error equal to is more efficient than the Bayesian network method.

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Sharif Journal of Industrial Engineering & Management

Articles in Press, Accepted Manuscript
Available Online from 19 March 2025

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