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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]

A synergistic perspective to selecting the right project portfolio is the key to realizing strategic organizational objectives. Based on the theory of the composite system, this paper identifies the factors that affect the science of project portfolio selection (PPS) and investigates the synergistic relationship among the organizational strategic objective subsystem, the project portfolio subsystem and the environment subsystem, which collectively constitute the composite system of project portfolio selection. Utilizing the concept of order parameters, an order parameter system is built for each subsystem to reflect the synergistic relationships in the composite system of PPS. The PPS model is constructed based on the degree of synergy in the composite system by describing the order degree of the subsystem and the synergy degree of the composite system, which helps to quantify the synergistic relationships among these subsystems. The effectiveness and feasibility of the proposed model are demonstrated and validated by a case study. [ABSTRACT FROM AUTHOR]