Multi‐Criteria Optimization of University Resource Allocation Using Hybrid AHP–TOPSIS and NSGA‐II Intelligent Search.
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...
| Publicado en: | Systems Research & Behavioral Science Vol. 43; no. 3; pp. 1314 - 1328 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
May/Jun2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193814466&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193814466 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10927026 2SN jtl: Systems Research & Behavioral Science issn: 10927026 maglogo: Y pubinfo: dt: May/Jun2026 vid: 43 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 193814466 10.1002/sres.70040 ppf: 1314 ppct: 14 formats: tig: atl: Multi‐Criteria Optimization of University Resource Allocation Using Hybrid AHP–TOPSIS and NSGA‐II Intelligent Search. aug: au: Wang, Shengjie Liang, Hongyun Zhou, Qilong affil: School of Information Engineering, Zhongyuan Institute of Science and Technology, Xuchang, China su: Resource allocation Diffusion of innovations Universities & colleges College teachers Systems theory Teaching methods Decision making Academic achievement Decision support systems Statistical correlation Management information systems Computer software Data analysis Systems development Paired comparisons (Mathematics) Analytic hierarchy process Mann Whitney U Test Descriptive statistics Surveys Conceptual structures Statistics Learning strategies Data analysis software Algorithms sug: subj: Resource allocation Diffusion of innovations Universities & colleges College teachers Systems theory Teaching methods Decision making Academic achievement Software publishers (except video game publishers) Computer and software stores Computer, computer peripheral and pre-packaged software merchant wholesalers Computer and Computer Peripheral Equipment and Software Merchant Wholesalers Colleges, Universities, and Professional Schools Decision support systems Statistical correlation Management information systems Computer software Data analysis Systems development Paired comparisons (Mathematics) Analytic hierarchy process Mann Whitney U Test Descriptive statistics Surveys Conceptual structures Statistics Learning strategies Data analysis software Algorithms keyword: AHP‐TOPSIS multi‐criteria decision making NSGA‐II Pareto optimization university resource allocation AHP‐TOPSIS multi‐criteria decision making NSGA‐II Pareto optimization university resource allocation ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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