Multi-criteria group decision-making with cloud model and TOPSIS for alternative selection under uncertainty

Abstract A novel hybrid soft computing approach that integrates the cloud model and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is developed to deal with multi-criteria decision making (MCDM) problems under uncertainty. Cloud models are used to derive linguistic concepts in numerical measures with the consideration of uncertainty. TOPSIS is used to determine the ranking of various alternatives with the evaluation matrices processed by the cloud model. The Monte Carlos simulation is implemented to model the uncertainty underlying the characterization of input factors and obtain their global sensitivities. An evaluation system with 16 criteria is formulated to discover the optimal alternative for tunnel excavation. A realistic tunnel section case in China is selected to demonstrate the adaptability and significance of the proposed approach. Results imply that (1) Among the 5 candidate tunneling methods, the shield tunnel boring machine (TBM) is determined as the optimal method to excavate the tunnel section in the case; (2) The inconsistency in results occur during the simulation in the presence of uncertainty, where there is 13% probability that soft-rock TBM will rank first in the case study; (3) With the increase of Entropy in cloud models, the degree of inconsistency will grow significantly. The developed approach can be used as a decision tool to provide insights into a better understanding of multi-source information fusion for alternative selection uncertainty with variations in the simulation and sensitivity analysis.