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Volume 8
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap’s Old Town

by
Rapeepan Pitakaso
1,
Thanatkij Srichok
1,
Surajet Khonjun
1,
Natthapong Nanthasamroeng
2,
Arunrat Sawettham
3,*,
Paweena Khampukka
3,
Sairoong Dinkoksung
3,
Kanya Jungvimut
4,
Ganokgarn Jirasirilerd
5,
Chawapot Supasarn
3,
Pornpimol Mongkhonngam
6 and
Yong Boonarree
4
1
Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
2
Artificial Intelligence Optimization SMART Laboratory, Engineering Technology Department, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand
3
Faculty of Management Science, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
4
Faculty of Applied Art and Architecture, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
5
Department of Industrial and Environmental Management Engineering, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, Thailand
6
Office of Research, Academic Services and Art & Culture Preservation, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
*
Author to whom correspondence should be addressed.
Heritage 2025, 8(8), 301; https://doi.org/10.3390/heritage8080301
Submission received: 8 June 2025 / Revised: 18 July 2025 / Accepted: 24 July 2025 / Published: 28 July 2025
(This article belongs to the Special Issue AI and the Future of Cultural Heritage)
Review Reports Versions Notes

Abstract

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities.
Keywords: heritage tourism; route optimization; deep reinforcement learning; metaheuristic; digital twin; multi-objective optimization; gan-based local search heritage tourism; route optimization; deep reinforcement learning; metaheuristic; digital twin; multi-objective optimization; gan-based local search

Share and Cite

MDPI and ACS Style

Pitakaso, R.; Srichok, T.; Khonjun, S.; Nanthasamroeng, N.; Sawettham, A.; Khampukka, P.; Dinkoksung, S.; Jungvimut, K.; Jirasirilerd, G.; Supasarn, C.; et al. A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap’s Old Town. Heritage 2025, 8, 301. https://doi.org/10.3390/heritage8080301

AMA Style

Pitakaso R, Srichok T, Khonjun S, Nanthasamroeng N, Sawettham A, Khampukka P, Dinkoksung S, Jungvimut K, Jirasirilerd G, Supasarn C, et al. A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap’s Old Town. Heritage. 2025; 8(8):301. https://doi.org/10.3390/heritage8080301

Chicago/Turabian Style

Pitakaso, Rapeepan, Thanatkij Srichok, Surajet Khonjun, Natthapong Nanthasamroeng, Arunrat Sawettham, Paweena Khampukka, Sairoong Dinkoksung, Kanya Jungvimut, Ganokgarn Jirasirilerd, Chawapot Supasarn, and et al. 2025. "A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap’s Old Town" Heritage 8, no. 8: 301. https://doi.org/10.3390/heritage8080301

APA Style

Pitakaso, R., Srichok, T., Khonjun, S., Nanthasamroeng, N., Sawettham, A., Khampukka, P., Dinkoksung, S., Jungvimut, K., Jirasirilerd, G., Supasarn, C., Mongkhonngam, P., & Boonarree, Y. (2025). A Hybrid Deep Reinforcement Learning and Metaheuristic Framework for Heritage Tourism Route Optimization in Warin Chamrap’s Old Town. Heritage, 8(8), 301. https://doi.org/10.3390/heritage8080301

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