Abstract
Priority vectors in the Analytic Hierarchy Process (AHP) are commonly estimated as constant values calculated by the pairwise comparison ratios elicited from an expert. For multiple experts, or panel data, or other data with varied characteristics of measurements, the priority vectors can be built as functions of the auxiliary predictors. For example, in multi-person decision making, the priorities can be obtained in regression modeling by the demographic and socio-economic properties. Then the priorities can be predicted for individual respondents, profiled by each predictor, forecasted in time, studied by the predictor importance, and estimated by the characteristic of significance, fit and quality well-known in regression modeling. Numerical results show that the suggested approaches reveal useful features of priority behavior, that can noticeably extend the AHP abilities and applications for numerous multiple-criteria decision making problems. The considered methods are useful for segmentation of the respondents and finding optimum managerial solutions specific for each segment. It can help to decision makers to focus on the respondents’ individual features and to increase customer satisfaction, their retention and loyalty to the promoted brands or products.
Highlights
The constant parameters of preference α j and αk we extend by substitution with the exponential functions: aijk
Vectors of priority calculatedusing using pairwise pairwise comparison matrices to find preferences among of priority arearecalculated comparison matrices to find preferences among different alternatives, and the elements of these vectors are commonly presented by the different alternatives, and the elements of these vectors are commonly presented by the constant values
A case of poorcan quality, a new eliciting could be needed, and the robust estimations priorities be applied as data eliciting could be needed, and the robust estimations of Analytic Hierarchy Process (AHP) priorities can be applied as well [26]
Summary
Analytic Hierarchy Process (AHP) is one of the main methods for solving various multiple-criteria decision making problems. It had been originated by Thomas Saaty [1,2,3,4,5]. The preferences in the AHP can be calculated by different techniques using the pairwise comparison matrices to find the vectors of priorities presented as constant values. Pair comparison techniques operate with multiple values, for example, of the AHP pairwise ratios, which are used for evaluation of the final priorities and the corresponding ranks of the preferences. Numerical results show that the suggested approaches reveal useful features of priority behavior that can noticeably extend the AHP abilities and applications for numerous multiple-criteria decision making problems.
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