Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty
The capacitated vehicle routing problem with dynamic demand and traffic conditions presents significant challenges in logistics and supply chain optimization. Traditional methods often fail to adapt to real-time uncertainties in customer demand and traffic patterns or scale to large problem instance...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Operations Research Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214716025000272 |
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| _version_ | 1849393648085499904 |
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| author | Shirali Kadyrov Azamkhon Azamov Yelbek Abdumajitov Cemil Turan |
| author_facet | Shirali Kadyrov Azamkhon Azamov Yelbek Abdumajitov Cemil Turan |
| author_sort | Shirali Kadyrov |
| collection | DOAJ |
| description | The capacitated vehicle routing problem with dynamic demand and traffic conditions presents significant challenges in logistics and supply chain optimization. Traditional methods often fail to adapt to real-time uncertainties in customer demand and traffic patterns or scale to large problem instances. In this work, we propose a deep reinforcement learning framework to learn adaptive routing policies for dynamic capacitated vehicle routing problem environments with stochastic demand and traffic. Our approach integrates graph neural networks to encode spatial problem structure and proximal policy optimization to train robust policies under both demand and traffic uncertainty. Experiments on synthetic grid-based routing environments show that our method outperforms classical heuristics and greedy baselines in minimizing travel cost while maintaining feasibility. The learned policies generalize to unseen demand and traffic scenarios and scale to larger graphs than those seen during training. Our results highlight the potential of deep reinforcement learning for real-world dynamic routing problems where both demand and traffic evolve unpredictably. |
| format | Article |
| id | doaj-art-8cadfdbc7a0148f499d38d1fc0e9fa27 |
| institution | Kabale University |
| issn | 2214-7160 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Operations Research Perspectives |
| spelling | doaj-art-8cadfdbc7a0148f499d38d1fc0e9fa272025-08-20T03:40:21ZengElsevierOperations Research Perspectives2214-71602025-12-011510035110.1016/j.orp.2025.100351Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertaintyShirali Kadyrov0Azamkhon Azamov1Yelbek Abdumajitov2Cemil Turan3New Uzbekistan University, Movarounnahr 1, Tashkent, 100000, Uzbekistan; Corresponding author.New Uzbekistan University, Movarounnahr 1, Tashkent, 100000, UzbekistanNew Uzbekistan University, Movarounnahr 1, Tashkent, 100000, UzbekistanSDU University, Abylai Khan 1/1, Kaskelen, 040900, KazakhstanThe capacitated vehicle routing problem with dynamic demand and traffic conditions presents significant challenges in logistics and supply chain optimization. Traditional methods often fail to adapt to real-time uncertainties in customer demand and traffic patterns or scale to large problem instances. In this work, we propose a deep reinforcement learning framework to learn adaptive routing policies for dynamic capacitated vehicle routing problem environments with stochastic demand and traffic. Our approach integrates graph neural networks to encode spatial problem structure and proximal policy optimization to train robust policies under both demand and traffic uncertainty. Experiments on synthetic grid-based routing environments show that our method outperforms classical heuristics and greedy baselines in minimizing travel cost while maintaining feasibility. The learned policies generalize to unseen demand and traffic scenarios and scale to larger graphs than those seen during training. Our results highlight the potential of deep reinforcement learning for real-world dynamic routing problems where both demand and traffic evolve unpredictably.http://www.sciencedirect.com/science/article/pii/S2214716025000272Deep reinforcement learningVehicle Routing ProblemDemand uncertaintyTraffic uncertaintyGraph neural networksProximal policy optimization |
| spellingShingle | Shirali Kadyrov Azamkhon Azamov Yelbek Abdumajitov Cemil Turan Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty Operations Research Perspectives Deep reinforcement learning Vehicle Routing Problem Demand uncertainty Traffic uncertainty Graph neural networks Proximal policy optimization |
| title | Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty |
| title_full | Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty |
| title_fullStr | Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty |
| title_full_unstemmed | Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty |
| title_short | Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty |
| title_sort | deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty |
| topic | Deep reinforcement learning Vehicle Routing Problem Demand uncertainty Traffic uncertainty Graph neural networks Proximal policy optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214716025000272 |
| work_keys_str_mv | AT shiralikadyrov deepreinforcementlearningfordynamicvehicleroutingwithdemandandtrafficuncertainty AT azamkhonazamov deepreinforcementlearningfordynamicvehicleroutingwithdemandandtrafficuncertainty AT yelbekabdumajitov deepreinforcementlearningfordynamicvehicleroutingwithdemandandtrafficuncertainty AT cemilturan deepreinforcementlearningfordynamicvehicleroutingwithdemandandtrafficuncertainty |