A Simulation-based Analytic Hierarchy Process (AHP) Method for Fund Allocation Assessment

Ruzanna Mat Jusoh (1), Fatin Amirah Ahmad Shukri (2), Sharifah Aishah Syed Ali (3), Fazilatulaili Binti Ali (4)
(1) Department of Defence Science, National Defence University of Malaysia, 57000 Sg Besi, Kuala Lumpur, Malaysia
(2) Centre for Defence Foundation Studies, National Defence University of Malaysia, 57000 Sg Besi, Kuala Lumpur, Malaysia
(3) Department of Defence Science, National Defence University of Malaysia, 57000 Sg Besi, Kuala Lumpur, Malaysia
(4) Department of Defence Science, National Defence University of Malaysia, 57000 Sg Besi, Kuala Lumpur, Malaysia
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How to cite (IJASEIT) :
Jusoh, Ruzanna Mat, et al. “A Simulation-Based Analytic Hierarchy Process (AHP) Method for Fund Allocation Assessment”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3, June 2023, pp. 986-92, doi:10.18517/ijaseit.13.3.18295.
The effectiveness of government financing is a challenge in various industries, including higher education universities. The funding source and the resources' size are the key determinants of quality education. The problems arise in multi-criteria decision-making, where many subjective opinions are needed from the experts. It is, therefore necessary to prioritize the limited budget available for important criteria. On the other hand, multi-criteria evaluation leads to technically rigorous and enlightened university budget decisions. This paper proposes the exploitation of the Analytic Hierarchy Process (AHP) in budget allocation at one of the public universities in Malaysia. This study’s participants were eight top management experts in managing expenditure at the faculty level. The findings showed that the most significant factors in deciding budget allocations are Teaching and Learning (0.30) and Maintenance (0.26). Furthermore, the most dominant sub-criteria were laboratory and equipment devices (S4) and training and conferences (S10), with a weighted mean of 0.682 and 0.664, respectively. The weights were aggregated by the geometric mean and median, as well as the simulated mean and median, which showed deviating results and rank reversals. The geometric mean weights differed significantly. In contrast, the aggregation using measures of the median was similar to the geometric median, with only a few rankings criteria differing. This highlights that the median score is significant in weight calculation.

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