International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 908KB)
Enhancing Customer Experience in Real-Time Travel Reservation Systems through AI-Powered Multi-Agent Systems for Dynamic Support Optimization
PDF (908KB), PP.29-45
Views: 0 Downloads: 0
Author(s)
Biman Barua
1,2,*
M. Shamim Kaiser
1
Index Terms
AI-Powered Multi-Agent Systems (MAS), Customer Experience Enhancement, Real-Time Travel Reservation, Dynamic Support Optimization, Context-Aware Systems
Abstract
This paper introduces a novel approach an AI-powered Multi-Agent System (MAS) for dynamically optimizing support to enhance real-time travel reservation-side customer experience. It has an architecture with specialized agents working together under a centralized agent manager, including natural language processing, booking, optimization, and context-aware modules. The system proposes to address common constraints encountered in traditional travel platforms: delayed response to user queries, ambiguity treated poorly, and adaptation to user preferences not incorporated. Through simulated environments and realistic use cases, the MAS enables complex travel requests to be dealt with, availability to be changed dynamically, and user satisfaction to be enhanced. The modular architecture design allows easy integration into larger smart tourism infrastructures. This study thus pushes the frontier further by merging AI, multi-agent collaboration, and user-centered design in a time-sensitive application world. Future directions include adaptive learning agents, multilingual interaction capabilities, and broadening the domain applications to hotel management and intelligent itinerary planning.
Cite This Paper
Biman Barua, M. Shamim Kaiser, "Enhancing Customer Experience in Real-Time Travel Reservation Systems through AI-Powered Multi-Agent Systems for Dynamic Support Optimization", International Journal of Education and Management Engineering (IJEME), Vol.16, No.3, pp. 29-45, 2026. DOI:10.5815/ijeme.2026.03.03
Reference
[1]Namjoshi, J., & Gupte, A. (2009, September). Service oriented architecture for cloud based travel reservation software as a service. In 2009 IEEE International Conference on Cloud Computing (pp. 147–150). IEEE.
[2]Joshi, P. K., Bubana, B., Gillarkar, T., Warade, P., Chetalangia, Y., & Qureshi, S. (2024, December). AirTech Odyssey: A web application for air travel reservations. In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 225–230). IEEE.
[3]Thakur, N. (2024). AI and technology are used in hospitality and tourism. In Hotel and Travel Management in the AI Era (pp. 55–78). IGI Global.
[4]Dubey, N., & Guleria, S. (2025). Determinants of tourist preferences: A comparative study of online and traditional travel agents in the era of digital consumerism. In Sustainability, Innovation, and Consumer Preference (pp. 227–248). IGI Global.
[5]Barua, B., & Kaiser, M. S. (2025). A Methodological Framework of Containerized Microservices Orchestration and Provisioning in the Cloud: A Case Study of Online Travel Platforms. In Challenges and Opportunities for Innovation in India (pp. 183-189). CRC Press.
[6]Suanpang, P., & Jamjuntr, P. (2024). Optimizing service scheduling by genetic algorithm support decision-making in smart tourism destinations. Decision Making: Applications in Management and Engineering, 7(1), 624–650.
[7]Aggrawal, S., Teotia, D., Yash, Y., & Shukla, S. (2025, February). Travel itinerary generation using AI and recommendation systems. In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 498–502). IEEE. https://doi.org/10.1109/CICTN57830.2025.00089
[8]Reis, J. (2024). Customer service through AI-powered human-robot relationships: Where are we now? The case of Henn na cafe, Japan. Technology in Society, 77, 102570. https://doi.org/10.1016/j.techsoc.2023.102570
[9]Chiwong, N., & Rojanavasu, P. (2020, March). Schedule travel planning system for Phayao information recommendation. In 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) (pp. 118–122). IEEE.
[10]Barua, B., & Kaiser, M. S. (2025). Optimizing Airline Reservation Systems With Edge-Enabled Microservices: A Framework for Real-Time Data Processing and Enhanced User Responsiveness.
[11]Song, Y., & He, Y. (2023). Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm. Soft Computing, 27(24), 19159–19177. https://doi.org/10.1007/s00500-023-08643-6
[12]Agarwal, K., Khare, O., Sharma, A., Prakash, A., & Shukla, A. K. (2024). Artificial intelligence in a distributed system of the future. In Decentralized Systems and Distributed Computing (pp. 317–335). Springer.
[13]Barua, B., & Kaiser, M. S. (2025). Novel Architecture for Distributed Travel Data Integration and Service Provision Using Microservices.
[14]Benferhat, S., & Boudjlida, A. (2011, November). Adding constraints and flexible preferences in travel reservation systems. In 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence (pp. 1063–1068). IEEE.
[15]Alfredo, Y. F., Girsang, A. S., Isa, S. M., & Fajar, A. N. (2018, September). Data warehouse development for flight reservation system. In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR) (pp. 134–140). IEEE.
[16]Ujwala, B. (2025). Cloud-enabled AI-driven interactive travel planning and booking system. International Journal of Computer Applications, 178(25), 45–52.
[17]Biman Barua, M. Shamim Kaiser, “Real-Time Performance Optimization of Travel Reservation Systems Using AI and Microservices,” Publication date: 17 Oct 2025, DOI: https://doi.org/10.1049/tje2.70139, Publisher: Wiley & Sons, Inc. Indexed in: Scopus, SCI.
[18]Mequanenit, A. M., Nibret, E. A., Herrero-Martín, P., García-González, M. S., & Martínez-Béjar, R. (2025). A multi-agent deep reinforcement learning system for governmental interoperability. Applied Sciences, 15(6), 3146. https://doi.org/10.3390/app15063146
[19]Barua, B., & Kaiser, M. S. (2026). Hybrid AI-microservices architectures for predictive maintenance in large-scale distributed systems. In Challenges and Opportunities in Artificial Intelligence (pp. 31-39). CRC Press.
[20]Sreeja, M. U., & Kovoor, B. C. (2017, August). Multi agent based extended travel support system using JADE framework. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3561–3564). IEEE.
[21]Barua, B., & Kaiser, M. S. (2025). A Next‐Generation Approach to Airline Reservations: Integrating Cloud Microservices With AI and Blockchain for Enhanced Operational Performance. IET Blockchain, 5(1), e70020.
[22]Kotaru, C., Udayaraju, P., Kolasani, D., Sayana, R., Tumkunta, S., & Gummadi, V. (2025, March). Behavioral pattern analysis framework with Markov chains and graph neural networks for scalable customer behavior prediction in travel reservations. In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS) (pp. 880–888). IEEE.
[23]Lv, X. (2021, March). Analysis and optimization strategy of travel hotel website reservation behavior based on collaborative filtering. In 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 362–365). IEEE.
[24]Barua, B., Barua, I., Kaiser, M. S., & Mozumder, M. J. U. (2025, February). Trends and Challenges in AI-Driven Microservices for Cloud-Based Airline Reservation Systems: A Review. In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) (pp. 1902-1911). IEEE.
[25]Barua, B., & Kaiser, M. S. (2026). AI-driven microservices for seamless user experience in multi-platform digital environments. In Challenges and Opportunities in Artificial Intelligence (pp. 107-114). CRC Press.
[26]Barua, B., Whaiduzzaman, M., Mesbahuddin Sarker, M., Shamim Kaiser, M., & Barros, A. (2023). Designing and Implementing a Distributed Database for Microservices Cloud-Based Online Travel Portal. In Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022 (pp. 295-314). Singapore: Springer Nature Singapore
[27]Barua, B., & Whaiduzzaman, M. (2019, July). A methodological framework on development the garment payroll system (GPS) as SaaS. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 431-435). IEEE
[28]Barua, B., & Kaiser, M. S. (2026). Enhancing fault tolerance in microservices with AI-powered anomaly detection and prediction. In Challenges and Opportunities in Artificial Intelligence (pp. 74-83). CRC Press.
[29]Barua, B., Mozumder, M. J. U., Kaiser, M. S., & Barua, I. (2025, February). Building Scalable Airlines Reservation Systems: A Microservices Approach Using AI and Deep Learning for Enhanced User Experience. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 941-950). IEEE.
[30]Chaki, P. K., Barua, B., Sazal, M. M. H., & Anirban, S. (2020, May). PMM: A model for Bangla parts-of-speech tagging using sentence map. In International Conference on Information, Communication and Computing Technology (pp. 181-194). Singapore: Springer Singapore.
[31]Barua, B., Aurpa, F. A., Mubarrat, M., Protul, N. I., Howlader, S. N., & Kaiser, M. S. (2025, August). AI for service-level agreement (SLA) optimization in microservices-based cloud infrastructures. In 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 485-492). IEEE.
[32]Mouri, I. J., Barua, B., Mesbahuddin Sarker, M., Barros, A., & Whaiduzzaman, M. (2023, March). Predicting Online Job Recruitment Fraudulent Using Machine Learning. In Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems: ICCCES 2022 (pp. 719-733). Singapore: Springer Nature Singapore.
[33]Nurbadi, R., Amyus, A., Pratama, B., Kanz, A. F., & Fajar, A. N. (2018, September). Smart travel system based on service oriented architecture. In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR) (pp. 256–261). IEEE.