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Risk Assessment of Equipment Research Project Costs Based on FAHP‐CRITIC Combined Weights for Two‐Dimensional Cloud Model
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Risk Assessment of Equipment Research Project Costs Based on FAHP‐CRITIC Combined Weights for Two‐Dimensional Cloud Model
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Journal Article
Risk Assessment of Equipment Research Project Costs Based on FAHP‐CRITIC Combined Weights for Two‐Dimensional Cloud Model
2024
Overview
The development of equipment research projects is replete with uncertainties, complicating the provision of accurate and objective evaluations of their development costs. To enhance the precision of cost predictions for such projects, it is critical to assess and integrate the cost‐risk level. This study introduces an improved two‐dimensional cloud model (TDCM) that effectively integrates qualitative concepts and quantitative data for analyzing equipment projects. First, the principal factors influencing cost are thoroughly evaluated, and an assessment index system is structured with three primary and twelve secondary indicators. The probability of budget overrun and its impact are designated as the primary variables for assessing each indicator, aligning with the definition of cost risk. Second, this research merges the fuzzy analytic hierarchy process (FAHP) with the criteria importance though intercriteria correlation (CRITIC) method, employing coalitional game theory to ascertain the weights of each assessment indicator. Third, a TDCM is developed to derive cloud eigenvalues, and a two‐dimensional cloud diagram is constructed through MATLAB to preliminarily ascertain the risk level, with the degree of nearness subsequently calculated to refine these results. Fourth, each indicator in the assessment system is treated as a node to construct a Bayesian network (BN) based on logical relationships, and a sensitivity analysis is conducted to identify sensitive indicators. Fifth, the development of a specific mine countermeasure (MCM) weapon system is examined as a case study, incorporating relevant existing data into the improved model. The validity and feasibility of the model are corroborated by comparing it with traditional methods. The results affirm that the enhanced TDCM effectively navigates the ambiguity and randomness inherent in cost‐risk assessment data, providing a reference for similar scientific research projects.
