Development of hybrid decision-making method for project management utilizing scheduling and line balancing risk assessment

This research focused on the uncertainty in the industrial project selection problem. In the real environment, several risk factors threaten the project's success which may lead to financial harm. However, such risk factors are not constant and may take various values depending on the project's environment. Therefore, the classic decision-making methods may fail to correctly address the actual risk factor values and select the best project among the alternatives. This research aims to find the critical risk factors that threaten a project through its life cycle and propose a decision-making method to select the best project with the lowest total risk factor in the presence of uncertainty. For this purpose, a multi-stage method is proposed where in the first stage, a Delphi method is used to determine influential factors that may affect project success through its life cycle. Then, a questionnaire is designed to find the opinion of the statistical society where project experts are considered as statistical society. The statistical analysis was then carried out to specify the variables' descriptions, find correlations between them, and determine their values in project success (as the dependent variable). Then, in the next stage, a new hybrid AHP and Dempster- Shafer (DS) Theory of Evidence is proposed, which was worked based on the uncertainty level of the risk factors. The proposed method could determine each alternative total risk level range and then report the best alternative with the lowest total risk level range. The findings showed that the risks could be divided into four main risk clusters, which are: Properties Risk Factors (Infrastructure, Machinery, Human Resource); Technology and Operational Risk Factors (Scheduling, Technology, Operational Risk, Management Systems); Financial Risk Factors (Evaluating projects, Profit, Costs, Money Value); Strategic Risk Factor (Competition, Market share, Marketing, Customer Satisfaction). In order to examine the performance of the proposed method, a Taguchi Method (L2^4) is designed for designing 24 experiments. The outcomes indicated that the proposed method could solve all small, medium, and large-scale experiments. Moreover, it could find the project with the lowest total risk range in all cases. While solving time comes into consideration, the proposed method solved the Small-scale problems in [0.036 0.054], Medium scale problems in [0.033 0.088], and Large-scale problems in [0.062 0.557] seconds, depending on the nature of the project. It is also noticeable that the proposed method could solve all experiments (including large-scale experiments with 30 experts, 13 risk factors, 10 alternatives and 5 contract options) in less than one second. The outcomes showed that the proposed hybrid method could select projects with the lowest total risk factor up to 90.53% for small-scale studied cases, up to 94.45% for medium-scale studied cases and 19.61% for large-scale studied cases depending to the scale of the case studies.

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