… the pairing delivery node. To solve this problem, we propose a deep reinforcement learning
… the policy network to capture and learn the precedence relation and heterogeneous roles of …

… delivery problem and its variants. We first discuss courier routing/ assignment literature and
then other delivery tasks (eg, delivery … for these problems, specifically reinforcement learning …

… In this paper, we propose a novel hierarchical reinforcement learning (RL) based optimization
framework to solve the real-world large-scale DPDPs. Considering that order dispatching …

… algorithms have been developed for solving similar problems, the novelty of this paper is …
that leverages deep reinforcement learning (DRL)—more specifically deep Q learning (DQN)—…

… In this paper, we propose a multi-agent reinforcement learning framework to solve
cooperative pickup and delivery problems (MAPDP). We design a special paired context embedding …

… Section 3 introduces the delivery and installation routing problem and the model structure.
In Section 4, we propose the designed step-wise deep reinforcement learning methods in …

… to address the order dispatching problem of a Turkish ultra-fast delivery company, Getir.
Before applying off-the-shelf reinforcement learning methods, we define the specific problem at …

… , multi-hop delivery, and profits. To incorporate the impact factors, we build a reinforcement
learning model to learn the optimal … algorithm to solve the routing problem of the packagers. …

… reinforcement learning (DRL) to solve NP-hard combinatorial optimization problems such as
routing problem, … reinforcement learning models are introduced in this section, respectively. …

… to solve this problem. To overcome this difficulty, we propose a novel reinforcement learning
… coupling relationships among the pickup and delivery customers, and the decoder process …