DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity
recently. To address the underlying task scheduling problem, conventional exact and …
recently. To address the underlying task scheduling problem, conventional exact and …
Solving the capacitated vehicle routing problem with time windows via graph convolutional network assisted tree search and quantum-inspired computing
J Dornemann - Frontiers in applied mathematics and statistics, 2023 - frontiersin.org
Vehicle routing problems are a class of NP-hard combinatorial optimization problems which
attract a lot of attention, as they have many practical applications. In recent years there have …
attract a lot of attention, as they have many practical applications. In recent years there have …
A deep learning Attention model to solve the Vehicle Routing Problem and the Pick-up and Delivery Problem with Time Windows
B Rabecq, R Chevrier - arXiv preprint arXiv:2212.10399, 2022 - arxiv.org
SNCF, the French public train company, is experimenting to develop new types of
transportation services by tackling vehicle routing problems. While many deep learning …
transportation services by tackling vehicle routing problems. While many deep learning …
DER-Solomon: A Large Number of CVRPTW Instances Generated Based on the Solomon Benchmark Distribution
The Solomon benchmark is a well-known resource for researching Capacitated Vehicle
Routing Problem with Time Windows (CVRPTW), and has been used by many traditional …
Routing Problem with Time Windows (CVRPTW), and has been used by many traditional …