A new constraint programming formulation for the electric-autonomous dial-a-ride problem
Journal of the Operational Research Society, 2026 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/01605682.2026.2697963
- Dergi Adı: Journal of the Operational Research Society
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, Periodicals Index Online, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Health Research Premium Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: constraint programming, DARP, Demand-Responsive Transport (DRT), Electric autonomous vehicles, integer programming, ride-sharing
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
This study investigates a new constraint-programming-based formulation for the electric-autonomous vehicle routeing problem in the context of a Demand-Responsive Transport (DRT) system. The Electric Autonomous Dial-A-Ride Problem (E-ADARP) involves planning a fleet of electric-autonomous vehicles to provide ride-sharing services for customers who specify their starting and destination points. E-ADARP considers the following perspectives: (i) minimizes total travel time; (ii) minimizes total excess ride-time; (iii) the use of electric autonomous vehicles and a partial charging policy. The main contribution of the study is the proposal of a novel constraint programming (CP) approach for modelling and solving the E-ADARP, and comparing it with an existing mixed-integer linear program (MILP). In particular, we formulate three constraint programs (CPs), test the performance of the proposed formulations on various data sets, and compare the resulting CPs using quality-of-solution metrics. We further investigate the potential effects of the CP approach by noting that it produces feasible solutions that can sometimes outperform those of the MILP in significantly shorter time. In addition, we analyse the impacts of including vehicle-related parameters such as battery level, capacity, and ride time. We note from the computational experiments that the proposed CP formulations yield promising results for solving large real-life problem instances.