Author(s)

Xianglin Du

Corresponding Author

Xianglin Du

Abstract

The large scale, complex systems, collaboration of multiple stakeholders, strong technical complexity, etc. of engineering projects make it difficult to control and evaluate the quality of supervision on these projects. The current quality evaluation systems of existing supervision work, that is indicator design, weight assignment and dynamic feedback mechanism are relatively limited, which makes it difficult to reflect the actual state of supervision work in complex projects. To this end, this paper first analyzes the relevant characteristics of complex engineering projects and their specific requirements for supervisor quality as well as problems existing in the current evaluation system. Then, a supervision quality assessment indicator system under three dimensions (technical dimension, manager aspect dimension and comprehensive dimension) is established. A method that combines Analytic Hierarchy Process (AHP) with Entropy Weighting Method is proposed to obtain comprehensive weight, thus improving the scientificity and objectivity of evaluation result. On top of this foundation, a comprehensive evaluation method measuring the supervision quality is constructed based on fuzzy comprehensive evaluation and improved TOPSIS model to obtain a relatively complete evaluation framework. Next, the potential application paths of information technology and big data in supervision quality evaluation and optimization are discussed, together with case analysis validating the feasibility and economic effectiveness of constructed system. This study indicates that the suggested model can not only improve the rigorousness and systematicness of evaluation for supervision quality, but also contribute to further dynamic optimizing and managing decision-making in complex engineering projects.

Keywords

complex engineering projects; supervision quality; evaluation system; optimization method; information technology application