An adaptive decision making method with copula Bayesian network for location selection

A novel multi-criteria decision making approach based on an adaptive copula Bayesian network (CBN) model is proposed to effectively handle complex dependence problems under uncertainty. Specifically, the Bayesian network is used to merge various criteria in a model and graphically describe cause-effect relationships. The copula is incorporated to construct joint distributions of variables by specifying marginal distributions and copula functions separately. Regarding the practical value, the constructed model can adjust to changeable conditions to provide adaptive suggestions in view of diverse disciplines. The effectiveness of the proposed approach is verified in a case study about choosing the most suitable location of the pedestrian overhead bridge (POB) to install lift facilities in Singapore. Firstly, three criteria and ten relevant influential factors concerning the sustainable aspect are derived from a combination of expert knowledge and statistical data. Then, a proper CBN model is developed under the consideration of these determined criteria and factors, aiming to predict the constructability index for alternative locations statistically. Finally, the correlation analysis and CBN inference are performed to evaluate and identify the ideal location in a data-driven manner. Furthermore, results from the proposed CBN-based approach are compared against the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to illustrate its advantages in representing dependency, modeling uncertainty, fusing information, and reducing subjectivity. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

A novel multi-criteria decision making approach based on an adaptive copula Bayesian network model is proposed to effectively handle complex dependence problems under uncertainty. The proposed approach is verified in a case study about choosing the most suitable location of pedestrian overhead bridge (POB) to install lift facilities in Singapore, showing its effectiveness in providing adaptive suggestions in view of diverse disciplines. The approach illustrates its advantages in representing dependency, modeling uncertainty, fusing information, and reducing subjectivity compared to the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS).