Assessing quality improvement initiatives when expert judgements are uncertain

A heterogeneous multi-criteria multi-expert decision-support system for scoring combinations of flood mitigation and recovery options

“…Second, the absence of vagueness in the preferences expressed by experts within each group (assumption 3) suggests the application of fuzzy sets only to combine preferences or indices in alternative forms, such as the BLTS method suggested by Herrera et al (2005) and applied by Halouani et al (2009), rather than using fuzzy sets to depict uncertainty, by avoiding the need to use the defuzzification centroid method (e.g., Vahdani et al, 2011). It would also be possible to apply possibility or probability approaches (e.g., Yuen and Lau, 2009), or to use geometric (e.g., Tan, 2011), recursive (e.g., Tsiporkova and Boeva, 2006), or stochastic judgment (e.g., Hahn and Knott, 2008) aggregation models.…”

“…Multi-criteria multi-expert decision-making (MCMEDM) is a methodology to deal with the inherent complexity and uncertainty of such problems as well as the vague knowledge arising from the participation of many experts in the decision-making process (Yan et al, 2011). The main examples of MCMEDM models are crisp or fuzzy "techniques for order performance by similarity to ideal solutions" (TOPSIS; e.g., Chen et al, 2011b), the "basic linguistic term set" (BLTS) method (Herrera et al, 2005), the defuzzification centroid method (e.g., Vahdani et al, 2011), crisp or fuzzy linear programming techniques (e.g., Bereketli et al, 2011), the cross-entropy approach (e.g., Ye, 2011), possibility or probability approaches (e.g., Yuen and Lau, 2009), and geometric (e.g., Tan, 2011) or recursive (e.g., Tsiporkova and Boeva, 2006) or stochastic (e.g., Hahn and Knott, 2008) judgement aggregation models.…”