reviews: 
Comments: This paper introduces a modified attention model tailored for the dynamic VRP, wherein orders are incrementally disclosed during the routing plan execution. The propsoed approach employs "dummy depots" as placeholders for yet-to-be-revealed positions, which are later substituted with specific newly revealed nodes (orders) to facilitate re-optimization of the route. Comparative results against LKH and OR-Tools are presented.
Strengths: * The paper addresses the dynamic CVRP, a subject of great significance for real-world applications.
* The authors have released several GIFs in the code repo.
Weaknesses: * The paper claims that "DRL for dynamic CVRP has not been well addressed yet." However, there seems to be a noticeable omission in the literature review. See the references below:
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[1] Peng, Bo, Jiahai Wang, and Zizhen Zhang. "A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems." Artificial Intelligence Algorithms and Applications: 11th International Symposium, ISICA 2019, Guangzhou, China, November 16–17, 2019, Revised Selected Papers 11. Springer Singapore, 2020.
[2] Joe, Waldy, and Hoong Chuin Lau. "Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers." Proceedings of the international conference on automated planning and scheduling. Vol. 30. 2020.
[3] Oren, Joel, et al. "SOLO: search online, learn offline for combinatorial optimization problems." Proceedings of the International Symposium on Combinatorial Search. Vol. 12. No. 1. 2021.
[4] Zhang, Zizhen, et al. "Solving dynamic traveling salesman problems with deep reinforcement learning." IEEE Transactions on Neural Networks and Learning Systems (2021).
[5] Li, Xijun, et al. "Learning to optimize industry-scale dynamic pickup and delivery problems." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021.
[6] Ma, Yi, et al. "A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems." Advances in Neural Information Processing Systems 34 (2021): 23609-23620.
[7] Basso, Rafael, et al. "Dynamic stochastic electric vehicle routing with safe reinforcement learning." Transportation research part E: logistics and transportation review 157 (2022): 102496.
[8] Pan, Weixu, and Shi Qiang Liu. "Deep reinforcement learning for the dynamic and uncertain vehicle routing problem." Applied Intelligence 53.1 (2023): 405-422.
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* Given the existing literature, the contributions presented in this paper appear limited. The concept of "dummy depots" isn't novel, and the proposed re-optimization process could be perceived as potentially time-intensive. Furthermore, the adaptation of using AM for dynamic VRP seems relatively direct and incremental.
* The experimental setup might benefit from scaling, as the current research is conducted on a rather limited size (with only 50 nodes), which might not sufficiently demonstrate the model's robustness.
* The manuscript's style and structure seem more suited for a journal than a conference like ICLR. Moreover, there are areas in the paper that appear to be incomplete. For instance, some details and formulas seem to be missing or inadequately explained. Repetitive paragraphs are in Section 3 and there are various typos.
* The design of action space might need reconsideration. In the context of dynamic VRP, it could be worth exploring whether vehicles should be permitted to wait at certain locations, anticipating the release of subsequent orders. This might enable more strategic route planning, taking into account potential future orders.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 1 poor
Clarity: 1 poor