A Partial-Consensus Posterior-Aggregation FAHP Method—Supplier Selection Problem as an Example

Abstract

Existing fuzzy analytic hierarchy process (FAHP) methods usually aggregate the fuzzy pairwise comparison results produced by multiple decision-makers (DMs) rather than the fuzzy weights estimations. This is problematic because fuzzy pairwise comparison results are subject to uncertainty and lack consensus. To address this problem, a partial-consensus posterior-aggregation FAHP (PCPA-FAHP) approach is proposed in this study. The PCPA-FAHP approach seeks a partial consensus among most DMs instead of an overall consensus among all DMs, thereby increasing the possibility of reaching a consensus. Subsequently, the aggregation result is defuzzified using the prevalent center-of-gravity method. The PCPA-FAHP approach was applied to a supplier selection problem to validate its effectiveness. According to the experimental results, the PCPA-FAHP approach not only successfully found out the partial consensus among the DMs, but also shrunk the widths of the estimated fuzzy weights to enhance the precision of the FAHP analysis.

A Pre-aggregation Fuzzy Collaborative Intelligence-based Fuzzy Analytic Hierarchy Process Approach for Selecting Alternative Suppliers amid the COVID-19 Pandemic

“…Most past FAHP studies did not check the existence of a consensus among experts, but directly aggregated the pairwise comparison results done by experts or the fuzzy priorities of criteria derived by them [15], which may lead to unacceptable results [17]. There are two time points at which the consensus among experts can be measured: before deriving the fuzzy priorities of criteria (i.e., the pre-aggregation way), and after deriving the fuzzy priorities of criteria (i.e., the post-aggregation way) [22].…”

“…Sometimes the pairwise comparison results by experts are aggregated before they derived the fuzzy priorities of criteria, i.e., the pre-aggregation way [5,[10][11][12]. There are also studies in which the fuzzy priorities of criteria derived by experts are aggregated instead, i.e., the post-aggregation way [13][14][15][16], is to aggregate the fuzzy priorities of criteria derived by experts. The pre-aggregation way eliminates the necessity for each expert to derive the fuzzy priorities of criteria individually, while the postaggregation way is easier to calculate.…”

“…Fuzzy collaborative intelligence (FCI) methods [18][19] adopt fuzzy intersection (FI) as the aggregator and can solve these problems naturally [20]. However, FCI methods have only been applied to post-aggregation problems [15][16] because the calculation is easier. To fill this gap, in this study, a pre-aggregation FCI-based FAHP approach is proposed.…”

A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice

“…After the negotiation process, the FI result of the fuzzy weights derived by all decision makers is adopted to represent their consensus [32][33][34][35][36]. When a consensus among all decision makers does not exist, an alternative is to seek for the consensus among only some of the decision makers [37].…”

Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach

“…(1) The fuzzy collaborative intelligence approach is a posterior-aggregation FAHP method, while most existing group-based FAHP methods are anterior-aggregation methods [20]; (2) In the fuzzy collaborative intelligence approach, decision makers' judgments are aggregated using FI, while in existing group-based FAHP methods, decision makers' judgement are usually aggregated using FGM. FI can help check the existence of a consensus, and is considered to be better than FGM [21]; (3) Although there have been some studies combining FAHP and fuzzy TOPSIS, these studies approximated, rather than derived, the values of fuzzy weights by applying FGM or fuzzy extent analysis (FEA) [22,23]. In contrast, the fuzzy collaborative intelligence approach derived the values of fuzzy weights by applying ACO;…”