Abstract:
Resource allocation in higher education is one of the most challenging decision-making problems due to the presence of multiple conflicting objectives and limited financial and operational resources. This study proposes an integrated framework that combines the Analytic Hierarchy Process (AHP) with Weighted Goal Programming (WGP) to support resource allocation decisions in higher education institutions. In the proposed approach, AHP is used to determine the relative priority weights of eight candidate projects based on three institutional criteria: teaching, research, and consultancy. The derived weights are then integrated into a Weighted Goal Programming model that allocates resources efficiently while satisfying financial, spatial, and operational constraints.
The proposed framework is applied to the real-world case study of Ho et al. (2007) and solved using Python with the PuLP optimization library. The results show that the AHP–WGP model selected five projects with a total budget utilization of 94.7%, compared to only four projects and 71% utilization achieved by the original Preemptive Goal Programming model. A comprehensive sensitivity analysis was conducted across three dimensions: budget variations of ±10%, alternative weighting using the TOPSIS method, and changes in minimum project requirements. The results confirmed the robustness and stability of the proposed model under different decision scenarios.
The findings demonstrate that the proposed AHP–WGP framework provides more flexible and balanced solutions, improves resource utilization, and better reflects real-world decision-making conditions compared to traditional Preemptive Goal Programming approaches. The study contributes to the growing body of literature on hybrid MCDM methods in higher education management and offers a practical decision-support tool for university administrators.
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