Learning to iteratively solve routing problems with dual-aspect collaborative transformer
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing
problems (VRPs). However, it is less effective in learning improvement models for VRP …
problems (VRPs). However, it is less effective in learning improvement models for VRP …
Learning improvement heuristics for solving routing problems
Recent studies in using deep learning (DL) to solve routing problems focus on construction
heuristics, whose solutions are still far from optimality. Improvement heuristics have great …
heuristics, whose solutions are still far from optimality. Improvement heuristics have great …
A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems
B Peng, J Wang, Z Zhang - … ISICA 2019, Guangzhou, China, November 16 …, 2020 - Springer
Recent researches show that machine learning has the potential to learn better heuristics
than the one designed by human for solving combinatorial optimization problems. The deep …
than the one designed by human for solving combinatorial optimization problems. The deep …
A lexicographic-based two-stage algorithm for vehicle routing problem with simultaneous pickup–delivery and time window
Vehicle routing problem with simultaneous pickup–delivery and time window (VRPSPDTW)
is computationally challenging as it generalizes the classical and NP-hard vehicle routing …
is computationally challenging as it generalizes the classical and NP-hard vehicle routing …
Reinforcement learning based truck-and-drone coordinated delivery
Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities
and medical items for home-quarantined people. Considering the unmanned operation …
and medical items for home-quarantined people. Considering the unmanned operation …
Towards omni-generalizable neural methods for vehicle routing problems
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
Learning to solve multiple-TSP with time window and rejections via deep reinforcement learning
We propose a manager-worker framework (the implementation of our model is publically
available at: https://github. com/zcaicaros/manager-worker-mtsptwr) based on deep …
available at: https://github. com/zcaicaros/manager-worker-mtsptwr) based on deep …
Learning to delegate for large-scale vehicle routing
Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical
applications. While previous heuristic or learning-based works achieve decent solutions on …
applications. While previous heuristic or learning-based works achieve decent solutions on …
Reinforcement learning for solving the vehicle routing problem
We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using
reinforcement learning. In this approach, we train a single policy model that finds near …
reinforcement learning. In this approach, we train a single policy model that finds near …
Efficiently solving the practical vehicle routing problem: A novel joint learning approach
Our model is based on the graph convolutional network (GCN) with node feature
(coordination and demand) and edge feature (the real distance between nodes) as input …
(coordination and demand) and edge feature (the real distance between nodes) as input …