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Multi‐Criteria Optimization of University Resource Allocation Using Hybrid AHP–TOPSIS and NSGA‐II Intelligent Search

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  • Shengjie Wang
  • Hongyun Liang
  • Qilong Zhou

Abstract

Strategic resource allocation in universities requires balancing academic performance, societal value and financial sustainability while accounting for heterogeneous stakeholder priorities. This research develops an integrated hybrid methodological framework in which preference structures are first derived using AHP from 100 stakeholder respondents, then evaluated and normalized using TOPSIS to generate proximity coefficients (range = 0.4167–0.6031), which subsequently guide a multi‐objective evolutionary search (NSGA‐II) rather than terminating in a deterministic ranking. The Pareto front reveals non‐linear trade‐off curvature, and the knee‐point allocation (FA = 0.1132; FB = 0.2714; FC = 0.2270; FD = 0.2666; FE = 0.1218) simultaneously maximized normalized academic (0.966) and societal (0.963) benefits while minimizing cost (0.004). Results confirm that preference‐based MCDM should not be used purely as a post hoc ranking generator but must be mathematically embedded into optimization dynamics to achieve governance‐compatible allocations that remain interpretable and stakeholder‐legitimate. The proposed hybrid architecture advances multi‐objective decision support for higher education management and provides a reproducible theoretical foundation for transparent resource planning beyond heuristic negotiation.

Suggested Citation

  • Shengjie Wang & Hongyun Liang & Qilong Zhou, 2026. "Multi‐Criteria Optimization of University Resource Allocation Using Hybrid AHP–TOPSIS and NSGA‐II Intelligent Search," Systems Research and Behavioral Science, Wiley Blackwell, vol. 43(3), pages 1314-1327, May.
    • Handle: RePEc:bla:srbeha:v:43:y:2026:i:3:p:1314-1327
    DOI: 10.1002/sres.70040
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