Cross-Domain AI Applications in Management Decision Optimization: Heuristic Algorithms in Practice

Shengyu Gu

doi:10.63313/EBM.9213

Authors

Shengyu Gu School of Geography and Tourism, Huizhou University, Huizhou, China Author

DOI:

https://doi.org/10.63313/EBM.9213

Keywords:

AI Decision Optimization, Heuristic Algorithms, Management Science, Cross-Domain Applications, Tourism Management, Healthcare Governance, Algorithmic Governance, Decision Performance, Institutional Usability

Abstract

This study proposes a unified framework of AI-driven management decision optimization based on heuristic algorithms and demonstrates its applicability across heterogeneous domains. Rather than treating artificial intelligence as a sector-specific technical artifact, the paper conceptualizes AI as a managerial decision engine that supports forecasting, optimization, risk control, and strategic coordination under uncertainty, resource constraints, and time pressure. Drawing on comparative evidence from tourism management and healthcare governance, the analysis shows that heuristic algorithms such as Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Simulated Annealing, and rule-based heuristics perform structurally similar decision functions despite differences in data content and institutional context. The core contribution lies in shifting evaluation criteria from domain accuracy toward governance-oriented performance, including decision quality, efficiency, transparency, responsiveness, and institutional impact. The findings demonstrate strong cross-domain transferability of heuristic decision logic and confirm that AI reshapes management reasoning itself rather than merely automating sectoral tools. Tourism is positioned as a methodological origin for decision optimization research, while healthcare serves as a high-stakes application context. Overall, the study establishes heuristic AI as a generalizable management science methodology for complex decision optimization across domains.

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