Search Results for author:
Found 3 papers, 0 papers with code
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems
We thus propose a Deep Dynamic Attention Models with Gate Mechanisms (DDAM-GM) to learn heuristics for time-dependent VRPs (TDVRPs) in real-world road networks.
Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda
Progress to-date towards the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets.
On scenario construction for stochastic shortest path problems in real road networks
We find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i. e., relative difference and variance) whichever origin-destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6-10 times lower for a stability level of 1\%; and (3) different origin-destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.
