A Comprehensive Decision Analysis Method Based on Analytic Hierarchy Process and Grey Prediction Model

Abstract

This paper proposes an integrated decision analysis framework combining the Analytic Hierarchy Process (AHP) with a grey prediction model, focusing on a unified modeling approach for matching different entities and forecasting trends under multi-objective constraints. First, a multi-level structural model based on AHP is constructed. Starting from weight allocation and consistency testing, it quantitatively evaluates the comprehensive performance of different entities under multi-dimensional indicators, enabling systematic identification of optimal matching targets. Second, a grey prediction model is introduced to model and forecast the trend changes of key indicators under limited sample conditions. The model's reliability and applicability are validated through accuracy testing and residual analysis, thereby delineating the system's future evolutionary direction. Finally, combining linear regression and time series analysis methods, the model quantitatively evaluates the effects of implemented intervention measures, forming a complete analytical process from prediction to decision-making to effect verification. This model features a clear structure, stable computation, and low sample size requirements. It enables effective prediction and decision support under conditions of incomplete information, demonstrating strong universality and promotional value. It provides a feasible and efficient modeling approach for the comprehensive analysis of similar complex systems.