An integrated framework based on multi-objective Markov decision processes (MOMDP) and receding horizon control (RHC) is proposed to address the dynamic optimization of sustainable tourism management. The core contribution lies in the development of a non-uniform grid state-space discretization strategy, which achieves computational simplification by balancing accuracy and efficiency, and in the use of backward induction for efficient policy derivation. Validation through case studies in Juneau, Alaska, and Maui, Hawaii, demonstrates that the framework significantly outperforms static baseline policies, ensuring computational feasibility, effectively balancing economic, environmental, and social objectives, and systematically establishing its strong generalization capability across diverse socio-ecological contexts.

Liang Jiaming. Research on Multi-Objective Markov Decision Making for Sustainable Tourism. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 10: 68-80. https://doi.org/10.25236/AJCIS.2025.081010.

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