No abstract

Logistics Park, as a large-scale construction project, has many risk factors that may affect the normal operation of the project in its construction. If the influence of risk factors is ignored, it will bring irreparable losses. Therefore, according to the characteristics of logistics park construction project (LPCP), it is of great practical significance to propose a new risk management model for the risk research of LPCP. Considering the whole project, this paper puts forward a new risk analysis model for construction projects, establishes a risk evaluation index system according to the causes and possible consequences of risks, describes the uncertainty and hesitation of failure mode and effects analysis (FMEA) team members’ risk evaluation information based on the uncertainty language Z number, calculates the expert weights by means of dynamic weight adjustment method, and then uses fuzzy C-means clustering algorithm to deal with the risk evaluation of LPCP.

PurposePrefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.Design/methodology/approachThe proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.FindingsThe proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.Originality/valueThe research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.