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With the advancement of urbanization, urban agglomerations have become a main carrier of China's development. As the ultimate stage of urban agglomeration, the realization of urban agglomeration integration (UAI) plays a dominant role in regional prosperity. Appropriate evaluation of UAI could monitor the integration level and guide its coordinated development. However, little attention has been paid to UAI evaluation research. To this end, this study proposes a comprehensive evaluation model to investigate UAI via the entropy method blended with TOPSIS. Furthermore, utilizing grey relational analysis (GRA), critical dimensions of UAI are also identified to provide concrete evidence. Finally, the proposed model is applied to evaluate the UAI of the Pearl River Delta (PRD), and the main results are as follows: (1) The development trend of the overall UAI in the PRD has increased in fluctuation during the research period, achieving a "good" degree; (2) Except for the policy measures integration (0.882), the integration values for other dimensions are in the range of 0.6 to 0.8 in 2020, reflecting a fairly high level; (3) The infrastructure integration has a major effect on UAI. These findings are favorable in providing references for understanding the current situation of UAI in China, which are of considerable practical significance. [ABSTRACT FROM AUTHOR]

Service provider portfolio selection (SPPS) can be a major challenge for organizations to achieve project success. Hence, organizations need to decide on which service provider portfolio (SPP) is appropriate for project management (PM). However, there has been limited research on how to select a SPP in PM. To address this research gap, we establish a novel model for SPPS based on a BP neural network integrated with entropy-AHP from the perspective of the comprehensive economic benefit. This model employs a BP neural network due to its robustness and memory and nonlinear mapping abilities. Furthermore, we implement the proposed model for a construction project to verify the effectiveness. Our results indicate that the model performs well with a prediction accuracy of 97%. Moreover, the model is confirmed to be robust as it still achieves high prediction accuracy when the input data are disturbed randomly. [ABSTRACT FROM AUTHOR]