Multi-criteria selection of underground utility tunnel ventilation fans based on an ANP-TOPSIS coupled model with interdependent indicators

Abstract

Traditional analytic hierarchy process (AHP) cannot handle the interdependence and feedback relationships among evaluation indicators, which often yields biased weight allocation and suboptimal rankings in equipment selection for underground utility tunnels. To solve this problem, this study developed a hybrid decision-making model integrating analytic network process (ANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ventilation fan optimal selection. The ANP was adopted to quantify complex indicator coupling relationships and generate globally convergent weights that correct the bias arising from the independence assumption of AHP, while the TOPSIS method was utilized to achieve accurate ranking of candidate ventilation fan schemes. A practical engineering case of an underground civil air defense utility tunnel was adopted for validation. The results indicated that equipment reliability, represented by mean time between failures, served as the dominant evaluation indicator, and the FKZ (K40)-8-No. 18 Ventilation fan was determined as the optimal scheme with a closeness coefficient of 0.6175. Comparative experiments revealed that the proposed ANP-TOPSIS model exhibited higher result discriminability and ranking accuracy than conventional AHP, standalone ANP, and equal-weight TOPSIS methods. The established model effectively addresses the multi-attribute decision-making dilemma with interdependent indicators and provides a reliable technical reference for the optimal selection and low-carbon operation of ventilation equipment in underground utility tunnels and similar confined engineering facilities.