A transformer-based deep reinforcement learning for the Dial-a-Ride problem
Knowledge and Information Systems, cilt.67, sa.10, ss.9085-9109, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 67 Sayı: 10
- Basım Tarihi: 2025
- Doi Numarası: 10.1007/s10115-025-02493-4
- Dergi Adı: Knowledge and Information Systems
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
- Sayfa Sayıları: ss.9085-9109
- Anahtar Kelimeler: Deep reinforcement learning, Dial-a-ride, Pickup and delivery tasks, Routing problem
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
Dial-a-Ride problem (DARP) is a specialized variant of the vehicle routing problem that focuses on designing optimal routes for users who specify pick-up and delivery requests between origins and destinations. This problem involves determining the most effective and efficient routes using limited vehicles and resources to offer responsive door-to-door transportation services for users with specific needs. Logistics service providers require systems that can deliver optimal solutions to these combinatorial problems within a reasonable time. Recently, there has been a significant increase in the use of artificial intelligence optimization algorithms such as meta-heuristics or learning-based approaches to solve such problems. Among the learning-based approaches, reinforcement learning has gained prominence for routing and scheduling tasks, owing to its ability to adaptively learn from complex state spaces and dynamically changing environments. In this study, a novel transformer-based deep reinforcement learning method is proposed to solve the Dial and Ride problem for a single service vehicle. In the proposed model, we adopt a modified transformer architecture instead of employing traditional linear layers, we integrate convolutional layers. To validate our approach, we conduct comprehensive experiments comparing our method against four well-known metaheuristic algorithms and a Deep Q-Network algorithm. The results indicate that proposed approach outperforms these techniques in terms of shorter total travel distances. Additionally, the proposed method is tested on a real-world scenario generated in the Buyukdere neighborhood of Eskisehir. The results demonstrate that the proposed method makes it possible to solve the problem within a reasonable time. This study confirms that the proposed deep reinforcement learning method can effectively address the Dial-a-Ride Problems.